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  1. Duration: 1h 36m | Video: .MP4, 1280x720, 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 837 MB Genre: eLearning | Language: English Data Visualization Essentials with Plotly and Python introduces you to the popular Plotly library for Python. In this course you'll learn how to create interactive plots with Plotly and Python. This is one of the most in demand skills required for a data science career path! This goal of this course is to bring your Data Visualization Skills to the next level to build your career in Data Science, Finance or Business Analytics. Since this is intermediate Python you are required to already master the basics of Python before enrolling into this class. Some basic knowledge of Pandas, the most used Python Library for Data Science is also required. Once you have a grasp on Python and Pandas Basics, you can move on to Plotly. My advice is to first check my other classes on Python and Pandas published here on SkillShare; they will help you build a strong foundation of Python Programming Language. In this course we'll get the skills to get ahead! Major topics of this course Installing and Using Jupyter Notebook. Installing Plotly in Python 3 Creating Scatter Plots Creating Line Charts PROJECT: Line Charts for Ethereum andTesla Stock Price PROJECT SOLUTION: Line Charts forEthereum and Tesla Stock Price Creating Basic Bar Charts Creating Basic Bar Charts Creating Pie Charts Creating Histograms and more! Homepage https://www.skillshare.com/classes/Data-Visualization-Essentials-with-Plotly-and-Python/945332078 Download from UploadCloud https://www.uploadcloud.pro/26mkqz9r9dy0/hzhmd.Data.Visualization.Essentials.with.Plotly.and.Python.rar.html Download ( Rapidgator ) https://rapidgator.net/file/b12cacb483f2d3d809cff6ea4a4e9382/hzhmd.Data.Visualization.Essentials.with.Plotly.and.Python.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/b7a0ba7977aa7784/hzhmd.Data.Visualization.Essentials.with.Plotly.and.Python.rar Download ( NitroFlare ) https://nitroflare.com/view/61A1206F11C086A/hzhmd.Data.Visualization.Essentials.with.Plotly.and.Python.rar Links are Interchangeable - No Password - Single Extraction
  2. Released 17/07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 162 Lessons (14h 59m) | Size: 3.6 GB This intermediate How to use Postman and Python for Data Center Automation training prepares network admins to write programs in Python and use Postman to communicate with services, systems and devices that make automating your data center's behavior a breeze Postman and Python are a perfect marriage because both are designed to address opposite sides of the same problem: how to take a large and complex system and simplify its operations and behavior. This course teaches you how to write code that manages and administers your data center's operations with Python and how to interact with APIs by using Postman. Take this course and see how great Python and Postman are on their own and how incredibly powerful they are when used together. Once you're done with this Cisco skills training, you'll know how to write programs in Python and use Postman to communicate with services, systems and devices that make automating your data center's behavior a breeze. For anyone who manages their Cisco training, this Cisco training can be used to onboard new network admins, curated into individual or team training plans, or as a Cisco reference resource. How to use Postman and Python for Data Center Automation: What You Need to Know This How to use Postman and Python for Data Center Automation training has videos that cover topics such as Interacting with REST APIs by using Postman Understanding various data formats, such as XML, JSON, and YAML Improving data center efficiency through automation Who Should Take How to use Postman and Python for Data Center Automation Training? This How to use Postman and Python for Data Center Automation training is considered associate-level Cisco training, which means it was designed for network admins. This data center automation skills course is designed for network admins with three to five years of experience with Cisco data center networking. New or aspiring network admins. This is a great course for new network administrators because it'll give you a way of thinking about data center automation that you can bring with you throughout your entire career. From here on out, you can come to work every day wondering if you can automate a portion of your job. Experienced network admins. It's never too late to learn about how much of your administration job can be done for you, and this Postman and Python course will be enhanced by a deep background in network administration. So network admins with several years of experience who take this course will find that it's a force multiplier for all their familiarity with data center administration, which can be automated. Homepage https://www.cbtnuggets.com/it-training/cisco/automate-data-centers Download ( Rapidgator ) https://rapidgator.net/file/1880249b7664d4bedadc42c491d2a87c/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part1.rar.html https://rapidgator.net/file/3c78f3a091f386d8928bd9308aa794ae/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part2.rar.html https://rapidgator.net/file/8c8c1713d62bf7262b4a488a682d3b11/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part4.rar.html https://rapidgator.net/file/c214fe7d57926aa75f0e19d9fb0ec6f0/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part3.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/0195172e864Bbd51/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part3.rar https://uploadgig.com/file/download/09f95b5e456a2ad6/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part4.rar https://uploadgig.com/file/download/80dc706ae32998C8/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part2.rar https://uploadgig.com/file/download/C92b70565ef245aA/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/3ED64A781085902/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part3.rar https://nitroflare.com/view/9B9D26AC988A8A4/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part4.rar https://nitroflare.com/view/ABDD3D6B034EC80/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part2.rar https://nitroflare.com/view/D87B5EDCFFA4A25/avlyh.CBTNuggets..How.to.use.Postman.and.Python.for.Data.Center.Automation.part1.rar Links are Interchangeable - No Password - Single Extraction
  3. Last updated 12/2020 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.78 GB | Duration: 7h 21m Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting What you'll learn Differentiate between time series data and cross-sectional data. Understand the fundamental assumptions of time series data and how to take advantage of them. Transforming a data set into a time-series. Start coding in Python and learn how to use it for statistical analysis. Carry out time-series analysis in Python and interpreting the results, based on the data in question. Examine the crucial differences between related series like prices and returns. Comprehend the need to normalize data when comparing different time series. Encounter special types of time series like White Noise and Random Walks. Learn about "autocorrelation" and how to account for it. Learn about accounting for "unexpected shocks" via moving averages. Discuss model selection in time series and the role residuals play in it. Comprehend stationarity and how to test for its existence. Acknowledge the notion of integration and understand when, why and how to properly use it. Realize the importance of volatility and how we can measure it. Forecast the future based on patterns observed in the past. Requirements No prior experience with time-series is required. You'll need to install Anaconda. We will show you how to do that step by step. Some general understanding of coding languages is preferred, but not required. Description How does a commercial bank forecast the expected performance of their loan portfolio?Or how does an investment manager estimate a stock portfolio's risk?Which are the quantitative methods used to predict real-estate properties?If there is some time dependency, then you know it - the answer is: time series analysis.This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also:· Easy to understand· Comprehensive· Practical· To the point· Packed with plenty of exercises and resourcesBut we know that may not be enough.We take the most prominent tools and implement them through Python - the most popular programming language right now. With that in mind.Welcome to Time Series Analysis in Python!The big question in taking an online course is what to expect. And we've made sure that you are provided with everything you need to become proficient in time series analysis.We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards.Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima.With these tools we will master the most widely used models out there:· AR (autoregressive model)· MA (moving-average model)· ARMA (autoregressive-moving-average model)· ARIMA (autoregressive integrated moving average model)· ARIMAX (autoregressive integrated moving average model with exogenous variables). SARIA (seasonal autoregressive moving average model). SARIMA (seasonal autoregressive integrated moving average model). SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)· ARCH (autoregressive conditional heteroscedasticity model)· GARCH (generalized autoregressive conditional heteroscedasticity model). VARMA (vector autoregressive moving average model)We know that time series is one of those topics that always leaves some doubts.Until now.This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials - notebooks files, course notes, quiz questions, and many, many exercises - everything is included.What you get?· Active Q&A support· Supplementary materials - notebook files, course notes, quiz questions, exercises· All the knowledge to get a job with time series analysis· A community of data science enthusiasts· A certificate of completion· Access to future updates· Solve real-life business cases that will get you the jobWe are happy to offer a 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.Why wait? Every day is a missed opportunity.Click the "Buy Now" button and start mastering time series in Python today. Overview Section 1: Introduction Lecture 1 What does the course cover? Lecture 2 Download Additional Resources Section 2: Setting Up the Environment Lecture 3 Setting up the environment - Do not skip, please! Lecture 4 Why Python and Jupyter? Lecture 5 Installing Anaconda Lecture 6 Jupyter Dashboard - Part 1 Lecture 7 Jupyter Dashboard - Part 2 Lecture 8 Installing the Necessary Packages Lecture 9 Installing Packages - Exercise Lecture 10 Installing Packages - Exercise Solution Section 3: Introduction to Time Series in Python Lecture 11 Introduction to Time-Series Data Lecture 12 Notation for Time Series Data Lecture 13 Peculiarities of Time Series Data Lecture 14 Loading the Data Lecture 15 Examining the Data Lecture 16 Plotting the Data Lecture 17 The QQ Plot Section 4: Creating a Time Series Object in Python Lecture 18 Transforming String inputs into DateTime Values Lecture 19 Using Date as an Index Lecture 20 Setting the Frequency Lecture 21 Filling Missing Values Lecture 22 Adding and Removing Columns in a Data Frame Lecture 23 Splitting Up the Data Lecture 24 Appendix: Updating the Dataset Section 5: Working with Time Series in Python Lecture 25 White Noise Lecture 26 Random Walk Lecture 27 Stationarity Lecture 28 Determining Weak Form Stationarity Lecture 29 Seasonality Lecture 30 Correlation Between Past and Present Values Lecture 31 The Autocorrelation Function (ACF) Lecture 32 The Partial Autocorrelation Function (PACF) Section 6: Picking the Correct Model Lecture 33 Picking the Correct Model Section 7: Modeling Autoregression: The AR Model Lecture 34 The Autoregressive (AR) Model Lecture 35 Examining the ACF and PACF of Prices Lecture 36 Fitting an AR(1) Model for Index Prices Lecture 37 Fitting Higher-Lag AR Models for Prices Lecture 38 Using Returns Instead of Prices Lecture 39 Examining the ACF and PACF of Returns Lecture 40 Fitting an AR(1) Model for Index Returns Lecture 41 Fitting Higher-Lag AR Models for Returns Lecture 42 Normalizing Values Lecture 43 Model Selection for Normalized Returns (AR) Lecture 44 Examining the AR Model Residuals Lecture 45 Unexpected Shocks from Past Periods Section 8: Adjusting to Shocks: The MA Model Lecture 46 The Moving Average (MA) Model Lecture 47 Fitting an MA(1) Model for Returns Lecture 48 Fitting Higher-Lag MA Models for Returns Lecture 49 Examining the MA Model Residuals for Returns Lecture 50 Model Selection for Normalized Returns (MA) Lecture 51 Fitting an MA(1) Model for Prices Lecture 52 Past Values and Past Errors Section 9: Past Values and Past Errors: The ARMA Model Lecture 53 The Autoregressive Moving Average (ARMA) Model Lecture 54 Fitting a Simple ARMA Model for Returns Lecture 55 Fitting a Higher-Lag ARMA Model for Returns - Part 1 Lecture 56 Fitting a Higher-Lag ARMA Model for Returns - Part 2 Lecture 57 Fitting a Higher-Lag ARMA Model for Returns - Part 3 Lecture 58 Examining the ARMA Model Residuals of Returns Lecture 59 ARMA for Prices Lecture 60 ARMA Models and Non-Stationary Data Section 10: Modeling Non-Stationary Data: The ARIMA Model Lecture 61 The Autoregressive Integrated Moving Average (ARIMA) Model Lecture 62 Fitting a Simple ARIMA Model for Prices Lecture 63 Fitting a Higher-Lag ARIMA Model for Prices - Part 1 Lecture 64 Fitting a Higher-Lag ARIMA Model for Prices - Part 2 Lecture 65 Higher Levels of Integration Lecture 66 Using ARIMA Models for Returns Lecture 67 Outside Factors and the ARIMAX Model Lecture 68 Seasonal Models - SARIMAX Lecture 69 Predicting Stability Section 11: Measuring Volatility: The ARCH Model Lecture 70 The Autoregressive Conditional Heteroscedasticity (ARCH) Model Lecture 71 Volatility Lecture 72 A More Detailed Look of the ARCH Model Lecture 73 The arch_model Method Lecture 74 The Simple ARCH Model Lecture 75 Higher-Lag ARCH Models Lecture 76 An ARMA Equivalent of the ARCH Model Section 12: An ARMA Equivalent of the ARCH: The GARCH Model Lecture 77 The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model Lecture 78 The ARMA and the GARCH Lecture 79 The Simple GARCH Model Lecture 80 Higher-Lag GARCH Models Lecture 81 An Alternative to the Model Selection Process Section 13: Auto ARIMA Lecture 82 Auto ARIMA Lecture 83 Preparing Python for Model Selection Lecture 84 The Default Best Fit Lecture 85 Basic Auto ARIMA Arguments Lecture 86 Advanced Auto ARIMA Arguments Lecture 87 The Goal Behind Modelling Section 14: Forecasting Lecture 88 Introduction to Forecasting Lecture 89 Simple Forecasting Returns with AR and MA Lecture 90 Intermediate ("MAX" Model) Forecasting Lecture 91 Advanced (Seasonal) Forecasting Lecture 92 Auto ARIMA Forecasting Lecture 93 Pitfalls of Forecasting Lecture 94 Forecasting Volatility Lecture 95 Forecasting Appendix: Multivariate Forecasting Section 15: Business Case Lecture 96 Business Case - A Look Into the Automobile Industry Lecture 97 Completing 100% Aspiring data scientists.,Programming beginners.,People interested in quantitative finance.,Programmers who want to specialize in finance.,Finance graduates and professionals who need to better apply their knowledge in Python. Homepage https://www.udemy.com/course/time-series-analysis-in-python/ Download ( Rapidgator ) https://rapidgator.net/file/525ab9a3628a366b3fa3565219f8e226/plbuy.Time.Series.Analysis.In.Python.part2.rar.html https://rapidgator.net/file/e6fa1d899d77852d1b035220a34f7823/plbuy.Time.Series.Analysis.In.Python.part1.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/020E750c5aD89C2e/plbuy.Time.Series.Analysis.In.Python.part1.rar https://uploadgig.com/file/download/8f913cF0351c790b/plbuy.Time.Series.Analysis.In.Python.part2.rar Download ( NitroFlare ) https://nitroflare.com/view/162FDF5D9144B4F/plbuy.Time.Series.Analysis.In.Python.part2.rar https://nitroflare.com/view/2C3A7C1B90F6026/plbuy.Time.Series.Analysis.In.Python.part1.rar Links are Interchangeable - No Password - Single Extraction
  4. Published 08/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 74 lectures (6h 57m) | Size: 3.9 GB The Ultimate Teachers guide to Python using Videos, Illustrations, Code Samples & Assignments What you'll learn Foundation of Python Programming Language Creating Variables and using Functions Strings and Numbers If Statements and Loops Data Collections - Lists, Tuples and Dictionaries File Handling & Error Handling Date and Time Build Custom Functions and Libraries Object Oriented Programming (OOP) Recursion Regular Expressions Common Algorithms (Bubble Sort, Linear Search, Binary Search) Password Hashing CRUD with SQLite Database Engine Introduction to GUI Requirements Be able to type using the keyboard Description If you are a teacher, parent or guardian, join me in this exciting teachers' guide on Python Programming that will assist you in teaching high school students. The instructional videos and text material have incorporated using simple English with terms that are easy to follow. Kindly note that only teachers, parents and guardians can create the account and purchase the course. Course Curriculum Variables and Functions Strings and Numbers If Statements Loops - For and While Data Collections - Lists, Tuples and Dictionaries Error Handling File Handling with JSON Build Custom Functions and Libraries Date and Time Object Oriented Programming Common Algorithms (Binary and Linear Search, Bubble Sort) Password Hashing CRUD with SQLite DB Engine Recursion Introduction to GUI What this course contains Easy to follow instructional videos 14 Chapters with both Instructional Videos and Textual Material containing high-quality illustrations 44 Python Code Samples 31 Assignments Software to Install To begin with, you may use the Free Online Code editor available in the Resource section of each chapter For additional practice, we recommend installing Mu: a simple Python editor for beginners Guaranteed Course Outcome Understanding of Programming Concepts using Real-World Examples. Promotes Creative Thinking and encourages the real-world application of coding skills Introduction to Object Oriented Programming and Databases The ability to build applications with ease. The Apps that can be built with the help of this course With 44 Code Samples and 31 Assignments, learners can attain the skills to create a wide selection of programs. The most notable ones are listed below Virtual Wardrobe Planner Match Roster Generator Guessing Number Game Multiplication Table Generator Number Divisibility Even and Odd Number Extraction The ATM Replica Automated Dice Roll Shopping List Phonetic App Jumble Words Generator Food Ordering App File Search Application Aspect Ratio Calculator Library Management System Authentication App with Password Hashing Can Teachers and Parents use this course as a guide for teaching kids younger than 17? Math is taught in every grade in school, the base concepts remain the same, but the complexity increases with every grade. The same learning progression can be achieved with programming languages for children. This course has been specifically designed as a teaching guide for parents, teachers and guardians who wish to teach high school students. How does this course compare with "Teach Python to Kids Age 11 to 13"? This course contains 7 additional lessons, with each existing lesson having additional content Libraries Date and Time Object Oriented Programming Common Algorithms (Binary and Linear Search, Bubble Sort) Password Hashing CRUD with SQLite DB Engine Introduction to GUI Also contains 16 Additional Code Samples and New Assignments Who this course is for Parents willing to teach their teenage children coding Teachers willing to teach coding to teenage students Homepage https://www.udemy.com/course/teach-python-to-high-school-students/ Download ( Rapidgator ) https://rapidgator.net/file/2a2e4cc43f84130558dcb6f767011242/xdlpk.Teach.Python.to.High.School.Students.part4.rar.html https://rapidgator.net/file/3977addbef8da2393146b783bfb520e2/xdlpk.Teach.Python.to.High.School.Students.part1.rar.html https://rapidgator.net/file/6cd3ee269c9baa486e127fd474fbfffb/xdlpk.Teach.Python.to.High.School.Students.part2.rar.html https://rapidgator.net/file/9c6cdcb987ea4e26522ef25a880cb882/xdlpk.Teach.Python.to.High.School.Students.part3.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/39a6058b282a11Fa/xdlpk.Teach.Python.to.High.School.Students.part2.rar https://uploadgig.com/file/download/A57158527170aDAC/xdlpk.Teach.Python.to.High.School.Students.part3.rar https://uploadgig.com/file/download/d636e1d2336abac8/xdlpk.Teach.Python.to.High.School.Students.part4.rar https://uploadgig.com/file/download/e4F95F8078aed9d4/xdlpk.Teach.Python.to.High.School.Students.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/2917EE03FF49A21/xdlpk.Teach.Python.to.High.School.Students.part3.rar https://nitroflare.com/view/58A2A2950703E43/xdlpk.Teach.Python.to.High.School.Students.part2.rar https://nitroflare.com/view/9C3073EA2F5BD4E/xdlpk.Teach.Python.to.High.School.Students.part4.rar https://nitroflare.com/view/AB33971C0E27D55/xdlpk.Teach.Python.to.High.School.Students.part1.rar Links are Interchangeable - No Password - Single Extraction
  5. Published 08/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 54 lectures (3h 58m) | Size: 2.9 GB Learn To Develop Your Python Project: Two Projects-Crypto Data Analysis & Amazon Spending Behavior Analysis. What you'll learn Learn Python Programming Learn Computer Programming Data Manipulation Using Pandas: Understand Pandas By Writing a Program that Analyze Your Online Spending Behavior How to Build a Python Project: Create a Crypto Coin Price Analysis System Data Visualization: Build Interactive Charts in Python in less than 10 lines Understand How Computer Program Represent Real World Objects: Python Basic And Advanced Data Types Build Python Project In Google Colab Requirements No prerequisites Description In the current tech-efficient world, computer programming knowledge is essential in almost all disciplines. For example, the fields of law, business, education, social service, and medicine are massively using programming languages. As Tim Cook recently tweeted, "Coding is one of the most valuable skills a person can learn ." Similar sentiments were expressed by other business leaders. I have designed this course anticipating the value of computer programming to people in almost all disciplines. In another word, if you are someone who wants to take a computer programming course for the first time, this is the place to begin. Also, if you have taken another programming language but are unfamiliar with Python, in particular, you will find a home while taking this course. By including the example from the real world, I have designed this course to ensure that you will apply the knowledge that you have gained in this course to your field of expertise. In this course, you will find detailed explanations of the way computer program works. In a nutshell, you have landed in a right place to learn to code beneficial in your personal and professional life. In this course, you will get an opportunity to work on two projects. First, you will work on analyzing Amazon's Spending Behavior. Second, you will work on Crypto Data Analysis. By doing both of these projects, you will not just understand Python Syntax. Rather, you will head towards developing your own Python project independently. Happy Learning and Coding! Who this course is for First Computer Programming course All Professional: Doctors, Lawyers, Business Person, Educators and more Homepage https://www.udemy.com/course/python-everybody/ Download ( Rapidgator ) https://rapidgator.net/file/3c2c15a3c073cb8bf5f98d10bbc25a1d/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part3.rar.html https://rapidgator.net/file/79d6da1a30291e55d4d37f95293f2646/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part2.rar.html https://rapidgator.net/file/7f5bb239b8e34e9c8ef5d2f22d756991/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part1.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/0f08B6a162bc7Db9/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part2.rar https://uploadgig.com/file/download/72811C4608f0ed58/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part3.rar https://uploadgig.com/file/download/c6205add1d7f4ed0/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/142B9F78EBBBD0F/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part3.rar https://nitroflare.com/view/40F54E568011A64/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part1.rar https://nitroflare.com/view/8F5F12126302747/mlgbp.Python.for.Everybody..Your.First.Computer.Programming.Class.part2.rar Links are Interchangeable - No Password - Single Extraction
  6. Published 08/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 23 lectures (3h 47m) | Size: 2.11 GB Learn the popular tools and libraries for AI, ML and Data Science in our all in one course What you'll learn Learn the most important tools for mastering AI, ML and Data Science Learn to use Pandas and Numpy Learn to use matplotlib and seaborn libraries Learn the fundamentals of Data analysis Requirements Basic knowledge of Python is required to complete this course Description Do you want to learn the most important tools of Artificial intelligence and Machine Learning? Then we've got a perfectly designed course for you. Artificial Intelligence has enabled the processing of a large number of data and its use in the domain. There are several tools, frameworks, and libraries available to data scientists and developers, but knowing when and how to use these tools is a must. This course on basic artificial intelligence tools will help you gain this knowledge practically. Major Topics This Course Covers Pandas and NumPy Matplotlib and Seaborn Scikit Learn and Scipy Exploratory Data Analysis To become a successful data scientist or developer, you need to master these above libraries. These AI libraries will help you build reliable AI projects. In this course, you'll learn how to use these tools to visualize data and prepare data sets for AI and machine learning projects. You'll also learn how to use these tools to build projects from scratch. In this course, we'll be covering topics that will help you learn how to validate AI models and interpret models. The course curriculum features real examples that will help you learn all the tool features efficiently. So why are you waiting now? Start your journey to become a complete AI specialist with this course! See You In Class! Who this course is for Anyone who wants to quickly master the most important tools for AI&ML will find this course very useful Homepage https://www.udemy.com/course/libraries-in-python-for-aiml-data-science/ Download ( Rapidgator ) https://rapidgator.net/file/0d1d65753ebeb94ad8ee6db57129a70d/scmlk.Libraries.in.Python.For.AIML..Data.Science.part3.rar.html https://rapidgator.net/file/8bf272329f36d93d67d3cb817b2ed90b/scmlk.Libraries.in.Python.For.AIML..Data.Science.part2.rar.html https://rapidgator.net/file/939865884213bf32a59f19788ec57b3e/scmlk.Libraries.in.Python.For.AIML..Data.Science.part1.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/54b433978A5d25e8/scmlk.Libraries.in.Python.For.AIML..Data.Science.part2.rar https://uploadgig.com/file/download/565C97ceb8a41e16/scmlk.Libraries.in.Python.For.AIML..Data.Science.part3.rar https://uploadgig.com/file/download/acc6b06b3434e343/scmlk.Libraries.in.Python.For.AIML..Data.Science.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/5E73C921C0E0536/scmlk.Libraries.in.Python.For.AIML..Data.Science.part3.rar https://nitroflare.com/view/DA6391DE6DA9FF5/scmlk.Libraries.in.Python.For.AIML..Data.Science.part1.rar https://nitroflare.com/view/F9E99C0CBE8E777/scmlk.Libraries.in.Python.For.AIML..Data.Science.part2.rar Links are Interchangeable - No Password - Single Extraction
  7. Last updated 11/2020 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.51 GB | Duration: 5h 11m Beginner and Advanced Customer Analytics in Python: PCA, K-means Clustering, Elasticity Modeling & Deep Neural Networks What you'll learn Master beginner and advanced customer analytics Learn the most important type of analysis applied by mid and large companies Gain access to a professional team of trainers with exceptional quant skills Wow interviewers by acquiring a highly desired skill Understand the fundamental marketing modeling theory: segmentation, targeting, positioning, marketing mix, and price elasticity; Apply segmentation on your customers, starting from raw data and reaching final customer segments; Perform K-means clustering with a customer analytics focus; Apply Principal Components Analysis (PCA) on your data to preprocess your features; Combine PCA and K-means for even more professional customer segmentation; Deploy your models on a different dataset; Learn how to model purchase incidence through probability of purchase elasticity; Model brand choice by exploring own-price and cross-price elasticity; Complete the purchasing cycle by predicting purchase quantity elasticity Carry out a black box deep learning model with TensorFlow 2.0 to predict purchasing behavior with unparalleled accuracy Be able to optimize your neural networks to enhance results Requirements You'll need to install Anaconda. We will show you how to do it in one of the first lectures of the course Basic Python programming A willingness and enthusiasm to learn and practice Description Data science and Marketing are two of the key driving forces that help companies create value and stay on top in today's fast-paced economy.Welcome to.Customer Analytics in Python - the place where marketing and data science meet!This course is the best way to distinguish yourself with a very rare and extremely valuable skillset.What will you learn in this course?This course is packed with knowledge, covering some of the most exciting methods used by companies, all implemented in Python.Since Customer Analytics is a broad topic, we have created 5 different parts to explore various sides of the analytical process. Each of them will have their strong sides and shortcomings. We will explore both sides of the coin for each part, while making sure to provide you with nothing but the most important and relevant information!Here are the 5 major parts:1. We will introduce you to the relevant theory that you need to start performing customer analyticsWe have kept this part as short as possible in order to provide you with more practical experience. Nonetheless, this is the place where marketing beginners will learn about the marketing fundamentals and the reasons why we take advantage of certain models throughout the course.2. Then we will perform cluster analysis and dimensionality reduction to help you segment your customersBecause this course is based in Python, we will be working with several popular packages - NumPy, SciPy, and scikit-learn. In terms of clustering, we will show both hierarchical and flat clustering techniques, ultimately focusing on the K-means algorithm. Along the way, we will visualize the data appropriately to build your understanding of the methods even further. When it comes to dimensionality reduction, we will employ Principal Components Analysis (PCA) once more through the scikit-learn (sklearn) package. Finally, we'll combine the two models to reach an even better insight about our customers. And, of course, we won't forget about model deployment which we'll implement through the pickle package.3. The third step consists in applying Descriptive statistics as the exploratory part of your analysisOnce segmented, customers' behavior will require some interpretation. And there is nothing more intuitive than obtaining the descriptive statistics by brand and by segment and visualizing the findings. It is that part of the course, where you will have the 'Aha!' effect. Through the descriptive analysis, we will form our hypotheses about our segments, thus ultimately setting the ground for the subsequent modeling.4. After that, we will be ready to engage with elasticity modeling for purchase probability, brand choice, and purchase quantityIn most textbooks, you will find elasticities calculated as static metrics depending on price and quantity. But the concept of elasticity is in fact much broader. We will explore it in detail by calculating purchase probability elasticity, brand choice own price elasticity, brand choice cross-price elasticity, and purchase quantity elasticity. We will employ linear regressions and logistic regressions, once again implemented through the sklearn library. We implement state-of-the-art research on the topic to make sure that you have an edge over your peers. While we focus on about 20 different models, you will have the chance to practice with more than 100 different variations of them, all providing you with additional insights!5. Finally, we'll leverage the power of Deep Learning to predict future behaviorMachine learning and artificial intelligence are at the forefront of the data science revolution. That's why we could not help but include it in this course. We will take advantage of the TensorFlow 2.0 framework to create a feedforward neural network (also known as artificial neural network). This is the part where we will build a black-box model, essentially helping us reach 90%+ accuracy in our predictions about the future behavior of our customers.An Extraordinary Teaching CollectiveWe at 365 Careers have 550,000+ students here on Udemy and believe that the best education requires two key ingredients: a remarkable teaching collective and a practical approach. That's why we ticked both boxes.Customer Analytics in Python was created by 3 instructors working closely together to provide the most beneficial learning experience.The course author, Nikolay Georgiev is a Ph.D. who largely focused on marketing analytics during his academic career. Later he gained significant practical experience while working as a consultant on numerous world-class projects. Therefore, he is the perfect expert to help you build the bridge between theoretical knowledge and practical application.Elitsa and Iliya also played a key part in developing the course. All three instructors collaborated to provide the most valuable methods and approaches that customer analytics can offer.In addition, this course is as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts, and course notes, as well as notebook files with comments, are just some of the perks you will get by enrolling.Why do you need these skills?1. Salary/Income - careers in the field of data science are some of the most popular in the corporate world today. All B2C businesses are realizing the advantages of working with the customer data at their disposal, to understand and target their clients better2. Promotions - even if you are a proficient data scientist, the only way for you to grow professionally is to expand your knowledge. This course provides a very rare skill, applicable to many different industries.3. Secure Future - the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you've probably heard of the number of jobs that will be automated soon, right? Well, the marketing department of companies is already being revolutionized by data science and riding that wave is your gateway to a secure future.Why wait? Every day is a missed opportunity.Click the "Buy Now" button and let's start our customer analytics journey together! Overview Section 1: Introduction Lecture 1 What Does the Course Cover Section 2: A Brief Marketing Introduction Lecture 2 Segmentation, Targeting, and Positioning Lecture 3 Marketing Mix Lecture 4 Physical and Online Retailers: Similarities and Differences Lecture 5 Price Elasticity Section 3: Setting up the Environment Lecture 6 Setting up the Environment - Do not Skip, Please! Lecture 7 Why Python and Why Jupyter Lecture 8 Installing Anaconda Lecture 9 Jupyter Dashboard - Part 1 Lecture 10 Jupyter Dashboard - Part 2 Lecture 11 Installing the Relevant Packages Lecture 12 Installing the Relevant Packages: Homework Lecture 13 Installing the Relevant Packages: Homework Solution Section 4: Segmentation Data Lecture 14 Getting to know the Segmentation Dataset Lecture 15 Importing and Exploring Segmentation Data Lecture 16 Standardizing Segmentation Data Section 5: Hierarchical Clustering Lecture 17 Hierarchical Clustering: Background Lecture 18 Hierarchical Clustering: Implementation and Results Section 6: K-Means Clustering Lecture 19 K-Means Clustering: Background Lecture 20 K-Means Clustering: Implementation Lecture 21 K-Means Clustering: Results Section 7: K-Means Clustering based on Principal Component Analysis Lecture 22 Principal Component Analysis: Background Lecture 23 Principal Component Analysis: Application Lecture 24 Principal Component Analysis: Homework Lecture 25 Principal Component Analysis: Results Lecture 26 K-Means Clustering with Principal Components: Application Lecture 27 K-Means Clustering with Principal Components: Results Lecture 28 K-Means Clustering with Principal Components: Results Homework Lecture 29 Saving the Models Section 8: Purchase Data Lecture 30 Purchase Analytics - Introduction Lecture 31 Getting to know the Purchase Dataset Lecture 32 Importing and Exploring Purchase Data Lecture 33 Applying the Segmentation Model Section 9: Descriptive Analyses by Segments Lecture 34 Segment Proportions Lecture 35 Purchase Occasion and Purchase Incidence Lecture 36 Purchase Occasion and Purchase Incidence Homework Lecture 37 Brand Choice Lecture 38 Dissecting the Revenue by Segment Section 10: Modeling Purchase Incidence Lecture 39 The Model: Binomial Logistic Regression Lecture 40 Prepare the Dataset for Logistic Regression Lecture 41 Model Estimation Lecture 42 Calculating Price Elasticity of Purchase Probability Lecture 43 Price Elasticity of Purchase Probability: Results Lecture 44 Purchase Probability by Segments Lecture 45 Purchase Probability by Segments - Homework Lecture 46 Purchase Probability Model with Promotion Lecture 47 Calculating Price Elasticities with Promotion Lecture 48 Calculating Price Elasticities (Without Promotion) - Homework Lecture 49 Comparing Price Elasticities with and without Promotion Section 11: Modeling Brand Choice Lecture 50 Brand Choice Models. The Model: Multinomial Logistic Regression Lecture 51 Prepare Data and Fit the Model Lecture 52 Interpreting the Coefficients Lecture 53 Own Price Brand Choice Elasticity Lecture 54 Cross Price Brand Choice Elasticity Lecture 55 Own and Cross-Price Elasticity by Segment Lecture 56 Own and Cross-Price Elasticity by Segment Homework Lecture 57 Own and Cross-Price Elasticity by Segment - Comparison Lecture 58 Own and Cross-Price Elasticity by Segment Homework 2 Section 12: Modeling Purchase Quantity Lecture 59 Purchase Quantity Models. The Model: Linear Regression Lecture 60 Preparing the Data and Fitting the Model Lecture 61 Calculating Price Elasticity of Purchase Quantity Lecture 62 Calculating Price Elasticity of Purchase Quantity: Homework Lecture 63 Price Elasticity of Purchase Quantity: Results Lecture 64 Price Elasticity of Purchase Quantity: Homework Section 13: Deep Learning for Conversion Prediction Lecture 65 Introduction to Deep Learning for Customer Analytics Lecture 66 Exploring the Dataset Lecture 67 How Are We Going to Tackle the Business Case Lecture 68 Why do We Need to Balance a Dataset Lecture 69 Preprocessing the Data for Deep Learning Lecture 70 Outlining the Deep Learning Model Lecture 71 Training the Deep Learning Model Lecture 72 Testing the Model Lecture 73 Obtaining the Probability of a Customer to Convert Lecture 74 Saving the Model and Preparing for Deployment Lecture 75 Predicting on New Data Lecture 76 Completing 100% People who want a career in Data Science,People who want a career in Business Intelligence,Individuals who are passionate about numbers and quant analysis,People working in Data Science looking to expand their knowledge into Marketing analytics,People working in Marketing, looking for career growth in the realms of Data Science Homepage https://www.udemy.com/course/customer-analytics-in-python/ Download ( Rapidgator ) https://rapidgator.net/file/2cde69cf06db9c24876d7044cd89ea4a/dffai.Customer.Analytics.In.Python.part1.rar.html https://rapidgator.net/file/60fb91b7ce132fb24c113ac0634ba29c/dffai.Customer.Analytics.In.Python.part2.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/B212b2d14781332e/dffai.Customer.Analytics.In.Python.part1.rar https://uploadgig.com/file/download/b644c16232E29718/dffai.Customer.Analytics.In.Python.part2.rar Download ( NitroFlare ) https://nitroflare.com/view/1E7D4EF0DC6D3E3/dffai.Customer.Analytics.In.Python.part1.rar https://nitroflare.com/view/DB09A82E00BADF8/dffai.Customer.Analytics.In.Python.part2.rar Links are Interchangeable - No Password - Single Extraction
  8. Last updated 1/2021 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 2.19 GB | Duration: 6h 51m A complete data science case study: preprocessing, modeling, model validation and maintenance in Python What you'll learn Improve your Python modeling skills Differentiate your data science portfolio with a hot topic Fill up your resume with in demand data science skills Build a complete credit risk model in Python Impress interviewers by showing practical knowledge How to preprocess real data in Python Learn credit risk modeling theory Apply state of the art data science techniques Solve a real-life data science task Be able to evaluate the effectiveness of your model Perform linear and logistic regressions in Python Requirements No prior experience is required. We will start from the very basics You'll need to install Anaconda and Python. We will show you how to do that step by step Description Brand new course!!Hi! Welcome to Credit Risk Modeling in Python. The only online course that teaches you how banks use data science modeling in Python to improve their performance and comply with regulatory requirements. This is the perfect course for you, if you are interested in a data science career. Here's why:· The instructor is a proven expert (PhD from the Norwegian Business school, who has taught in world renowned universities such as HEC, the University of Texas, and the Norwegian Business school).· The course is suitable for beginners. We start with theory and initial data pre-processing and gradually solve a complete exercise in front of you· Everything we cover is up-to-date and relevant in today's development of Python models for the banking industry· This is the only online course that shows the complete picture in credit risk in Python (using state of the art techniques to model all three aspects of the expected loss equation - PD, LGD, and EAD) including creating a scorecard from scratch· Here we show you how to create models that are compliant with Basel II and Basel III regulations that other courses rarely touch upon· We are not going to work with fake data. The dataset used in this course is an actual real-world example· You get to differentiate your data science portfolio by showing skills that are highly demanded in the job marketplace· What is most important - you get to see first-hand how a data science task is solved in the real-worldMost data science courses cover several frameworks, but skip the pre-processing and theoretical part. This is like learning how to taste wine before being able to open a bottle of wine.We don't do that. Our goal is to help you build a solid foundation. We want you to study the theory, learn how to pre-process data that does not necessarily come in the ''friendliest'' format, and of course, only then we will show you how to build a state of the art model and how to evaluate its effectiveness.Throughout the course, we will cover several important data science techniques.- Weight of evidence- Information value- Fine classing- Coarse classing- Linear regression- Logistic regression- Area Under the Curve- Receiver Operating Characteristic Curve- Gini Coefficient- Kolmogorov-Smirnov- Assessing Population Stability- Maintaining a modelAlong with the video lessons you will receive several valuable resources that will help you learn as much as possible:· Lectures· Notebook files· Homework· Quiz questions· Slides· Downloads· Access to Q&A where you could reach out and contact the course tutor.Signing up for the course today could be a great step towards your career in data science. Make sure that you take full advantage of this amazing opportunity!See you on the inside! Overview Section 1: Introduction Lecture 1 What does the course cover Lecture 2 What is credit risk and why is it important? Lecture 3 Expected loss (EL) and its components: PD, LGD and EAD Lecture 4 Capital adequacy, regulations, and the Basel II accord Lecture 5 Basel II approaches: SA, F-IRB, and A-IRB Lecture 6 Different facility types (asset classes) and credit risk modeling approaches Section 2: Setting up the working environment Lecture 7 Setting up the environment - Do not skip, please! Lecture 8 Why Python and why Jupyter Lecture 9 Installing Anaconda Lecture 10 Jupyter Dashboard - Part 1 Lecture 11 Jupyter Dashboard - Part 2 Lecture 12 Installing the sklearn package Section 3: Dataset description Lecture 13 Our example: consumer loans. A first look at the dataset Lecture 14 Dependent variables and independent variables Section 4: General preprocessing Lecture 15 Importing the data into Python Lecture 16 Preprocessing few continuous variables Lecture 17 Preprocessing few continuous variables: Homework Lecture 18 Preprocessing few discrete variables Lecture 19 Check for missing values and clean Lecture 20 Check for missing values and clean: Homework Section 5: PD Model: Data Preparation Lecture 21 How is the PD model going to look like? Lecture 22 Dependent variable: Good/ Bad (default) definition Lecture 23 Fine classing, weight of evidence, and coarse classing Lecture 24 Information value Lecture 25 Data preparation. Splitting data Lecture 26 Data preparation. An example Lecture 27 Data preparation. Preprocessing discrete variables: automating calculations Lecture 28 Data preparation. Preprocessing discrete variables: visualizing results Lecture 29 Data preparation. Preprocessing discrete variables: creating dummies (Part 1) Lecture 30 Data preparation. Preprocessing discrete variables: creating dummies (Part 2) Lecture 31 Data preparation. Preprocessing discrete variables. Homework. Lecture 32 Data preparation. Preprocessing continuous variables: Automating calculations Lecture 33 Data preparation. Preprocessing continuous variables: creating dummies (Part 1) Lecture 34 Data preparation. Preprocessing continuous variables: creating dummies (Part 2) Lecture 35 Data preparation. Preprocessing continuous variables: creating dummies. Homework Lecture 36 Data preparation. Preprocessing continuous variables: creating dummies (Part 3) Lecture 37 Data preparation. Preprocessing continuous variables: creating dummies. Homework Lecture 38 Data preparation. Preprocessing the test dataset Lecture 39 PD model: data preparation notebooks Section 6: PD model estimation Lecture 40 The PD model. Logistic regression with dummy variables Lecture 41 Loading the data and selecting the features Lecture 42 PD model estimation Lecture 43 Build a logistic regression model with p-values Lecture 44 Interpreting the coefficients in the PD model Section 7: PD model validation Lecture 45 Out-of-sample validation (test) Lecture 46 Evaluation of model performance: accuracy and area under the curve (AUC) Lecture 47 Evaluation of model performance: Gini and Kolmogorov-Smirnov Section 8: Applying the PD Model for decision making Lecture 48 Calculating probability of default for a single customer Lecture 49 Creating a scorecard Lecture 50 Calculating credit score Lecture 51 From credit score to PD Lecture 52 Setting cut-offs Lecture 53 Setting cut-offs. Homework Lecture 54 PD model: logistic regression notebooks Section 9: PD model monitoring Lecture 55 PD model monitoring via assessing population stability Lecture 56 Population stability index: preprocessing Lecture 57 Population stability index: calculation and interpretation Lecture 58 Homework: building an updated PD model Section 10: LGD and EAD Models: Preparing the data Lecture 59 LGD and EAD models: independent variables. Lecture 60 LGD and EAD models: dependent variables Lecture 61 LGD and EAD models: distribution of recovery rates and credit conversion factors Section 11: LGD model Lecture 62 LGD model: preparing the inputs Lecture 63 LGD model: testing the model Lecture 64 LGD model: estimating the accuracy of the model Lecture 65 LGD model: saving the model Lecture 66 LGD model: stage 2 - linear regression Lecture 67 LGD model: stage 2 - linear regression evaluation Lecture 68 LGD model: combining stage 1 and stage 2 Lecture 69 Homework: building an updated LGD model Section 12: EAD model Lecture 70 EAD model estimation and interpretation Lecture 71 EAD model validation Lecture 72 Homework: building an updated EAD model Section 13: Calculating expected loss Lecture 73 Calculating expected loss Lecture 74 Homework: calculate expected loss on more recent data Lecture 75 Completing 100% You should take this course if you are a data science student interested in improving their skills,You should take this course if you want to specialize in credit risk modeling,The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills,This course is for you if you want a great career Homepage https://www.udemy.com/course/credit-risk-modeling-in-python/ Download ( Rapidgator ) https://rapidgator.net/file/3380c38e58dd643983d7e41554ae8f2e/rtbjc.Credit.Risk.Modeling.In.Python.2022.part1.rar.html https://rapidgator.net/file/8abd9c40dbb7c0d2df6356e6ec67f189/rtbjc.Credit.Risk.Modeling.In.Python.2022.part3.rar.html https://rapidgator.net/file/f8264a9ec454b8ccb7c304d8f067ef3f/rtbjc.Credit.Risk.Modeling.In.Python.2022.part2.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/9f3dfdb42a375b93/rtbjc.Credit.Risk.Modeling.In.Python.2022.part1.rar https://uploadgig.com/file/download/F2F73cc1e3525d1C/rtbjc.Credit.Risk.Modeling.In.Python.2022.part3.rar https://uploadgig.com/file/download/e928cd2F4F8202d1/rtbjc.Credit.Risk.Modeling.In.Python.2022.part2.rar Download ( NitroFlare ) https://nitroflare.com/view/4D951CB3EE6AC61/rtbjc.Credit.Risk.Modeling.In.Python.2022.part3.rar https://nitroflare.com/view/9DD47E106704BE2/rtbjc.Credit.Risk.Modeling.In.Python.2022.part2.rar https://nitroflare.com/view/E68D90F6C370BEC/rtbjc.Credit.Risk.Modeling.In.Python.2022.part1.rar Links are Interchangeable - No Password - Single Extraction
  9. Published 07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 29 lectures (3h 34m) | Size: 1.5 GB Learn Python OOP and Improve Your Python Programming Skills by Writing Code That Is Readable, Modular, and Reusable What you'll learn Understand the importance of using object-oriented programming Learn basic principles on how to build programs faster using Python How to use and apply polymorphism in object-oriented styles with many strategies Practice Object-oriented programming from basics to advanced level using modern Python Learn encapsulation and discover how to wrap data and codes together into a single unit Basic knowledge of how to abstract a problem in object-oriented programming in action Requirements Experience with the basics of coding in Python Basic mathematical skills Readiness, flexibility, and passion for learning Description Object-oriented programming (OOP) in Python is important for any software developer because it has a wide usage in the industry. It is a course any software engineers or aspiring software engineers need to put on their catalog to learn and master well because of its importance. OOP utilizes the concept of objects and classes and it is an important programming model for representing real objects as software objects. As a software developer or engineer, you need to learn an important concept in this type of programming putting into consideration many practical examples. This course features various exercises that will help you learn object-oriented programming in Python, and build programs faster. This object-oriented programming course exists for software engineers and developers because of its significance and numerous advantages in the software industry. Furthermore, it is a popular programming model with its many advantages and vital existence in every aspect of software apart from the fact that it's easy to learn, read and understand within a short time. The course put more focus on the thinking process, the implementation of what the developer is trying to manipulate other than the required logic to manipulate it. Why should I learn this course? Modularity: encapsulation enables objects to be self-contained and gives a clear modular structure for collaboration and troubleshooting code. Easy programming: It makes programming easier, and use for solving problems in a more creative process by breaking the program into bit-sized problems which are then solved easily. Increase productivity: Object-oriented programming guarantees greater programmer productivity, better quality software, and a low cost of maintenance. Learn programming languages to help you create outstanding professional programs and codes that are understandable, adaptable, and extendable. Learning from professionals: the program is designed to be learned from a pool of experienced experts in the field of software programming. The course is integrated with the practical experience of well-known experts which makes learning easier. The Ultimate Object-Oriented Programming in Python Course for Beginners Learning Object-oriented programming as a beginner requires an understanding of how to implement and take advantage of the features that are available in OOP (Object-oriented programming) which can be discouraging. Having an Insight into the various functionalities of Object-oriented programming is very essential since it's one of the most prominent coding methods which allows programmers to create a unique structure, reuse code, and develop code faster. Who this course is for Programmers and anyone that wants to learn how to code faster Software Developers and engineers Anybody inclined to learn and practice Object-oriented programming Python developers Homepage https://www.udemy.com/course/python-for-object-oriented-programming/ Download ( Rapidgator ) https://rapidgator.net/file/120447ec17f09efc1ed4df8ad3a0d783/oqvwy.Python.for.ObjectOriented.Programming.The.AtoZ.Course.part2.rar.html https://rapidgator.net/file/2a56e8642f021d12e2ef8060f1dbbfcf/oqvwy.Python.for.ObjectOriented.Programming.The.AtoZ.Course.part1.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/1e1CeD46cb8D4a8b/oqvwy.Python.for.ObjectOriented.Programming.The.AtoZ.Course.part2.rar https://uploadgig.com/file/download/B888d8DEc8c02c7c/oqvwy.Python.for.ObjectOriented.Programming.The.AtoZ.Course.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/58389CB40855C2B/oqvwy.Python.for.ObjectOriented.Programming.The.AtoZ.Course.part1.rar https://nitroflare.com/view/B724BA67A0280C1/oqvwy.Python.for.ObjectOriented.Programming.The.AtoZ.Course.part2.rar Links are Interchangeable - No Password - Single Extraction
  10. Published 08/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 44 lectures (16h 9m) | Size: 5.86 GB Learn the basics of Python in a nutshell ! Easy, quick "chillout" and with humor and... in the dancing way! What you'll learn You will learn the Python syntax (the mini-course covers about 90% of the Python language) You will learn the basics of Python with practical examples! You will learn to write in the Pycharm IDE (the most popular Python IDE) You will learn the basics of writing your own scripts and functions, all in a 'chillout' atmosphere You will learn the engineering thinking (programmers) when creating software (simple programs in practice!) Requirements Willingness to develop digital skills Basic knowledge of IT terms, curiosity + commitment Description - If you need a quick refresher or want to quickly learn the basics of Python, you've come to the right place! - Learn with me the basics of one of the easiest programming languages! If you have never coded, this is the perfect time to get started, after completing this course you will learn about 90% of the Python contribution. This is enough to start using this language in practice :) - Due to its structure, the Python programming language is beginner-friendly, and the spectrum of using Python in many fields of engineering gives you (with knowledge of Python) many possibilities for automation and reduction of time for previously performed manual activities on your computer. - Python is used in many commercial projects and is gaining popularity very quickly since 2020 it is the most popular programming language in the world! - This course will be a quick and easy way to understand all the major programming concepts in this language. - The course covers everything you need to know before applying the language to commercial projects. Below you will find the exact scope of the material that has been included in the course. In addition, in the course you will receive additional materials for download, e.g. the full source code of the course and the most popular Python cheet sheets in the world, translated by me into Polish after verification by experts in this field! - I think you will love to learn the basics of Python with me thanks to this course in a "chillout" atmosphere, and with an informal approach - in a friendly manner;) . Well, why wait, get on board and let's get started! The scope of the material for the course: Introduction to programming - in progress Be sure to join us, :) Chris Who this course is for Beginner testers, Beginner programmers, Beginner managers, "digital" professions requiring basic Python programming skills Users willing to learn the syntax and basics of Python Homepage https://www.udemy.com/course/python-3-learning-by-doing/ Download ( Rapidgator ) https://rapidgator.net/file/171c4ea2b1255e6d8f366e57a8a39618/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part4.rar.html https://rapidgator.net/file/3c5e528a86cdc02d65535c7ef1abd582/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part2.rar.html https://rapidgator.net/file/40e7be688ba04fff678208f635373a4c/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part3.rar.html https://rapidgator.net/file/c667c4b3bbbb5a90a411692d3741364c/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part5.rar.html https://rapidgator.net/file/dcd6bbd1c5f89b3c424726eea501bd39/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part6.rar.html https://rapidgator.net/file/e35ed654c3fc17178df192ddab28ef04/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part1.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/2d4f2f55b5C85118/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part6.rar https://uploadgig.com/file/download/4B0aF9bC1ddb95e0/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part2.rar https://uploadgig.com/file/download/4a1207B1d93BA730/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part4.rar https://uploadgig.com/file/download/54dc79007dc37dE2/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part1.rar https://uploadgig.com/file/download/e3Fd6952725dd0ab/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part5.rar https://uploadgig.com/file/download/e99C1cf62493f078/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part3.rar Download ( NitroFlare ) https://nitroflare.com/view/1E87D42985EE270/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part4.rar https://nitroflare.com/view/7E0B0D142618F9C/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part5.rar https://nitroflare.com/view/8E2FCB4BDA770DD/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part1.rar https://nitroflare.com/view/9F8945CB6A86799/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part2.rar https://nitroflare.com/view/BEF9A53A13CC0CD/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part6.rar https://nitroflare.com/view/E1F4E55286BE59F/mwihx.Python.3..Learning.by.doing.in.the.dancing.way.part3.rar Links are Interchangeable - No Password - Single Extraction
  11. Last updated 6/2021 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.80 GB | Duration: 5h 15m See the full picture: Learn how to combine the three most important tools in data science: Python, SQL, and Tableau What you'll learn How to use Python, SQL, and Tableau together Software integration Data preprocessing techniques Apply machine learning Create a module for later use of the ML model Connect Python and SQL to transfer data from Jupyter to Workbench Visualize data in Tableau Analysis and interpretation of the exercise outputs in Jupyter and Tableau Requirements Basic coding skills in Python Basic knowledge of SQL Basic ability to use Tableau for data visualization Description Python, SQL, and Tableau are three of the most widely used tools in the world of data science. Python is the leading programming language;SQL is the most widely used means for communication with database systems;Tableau is the preferred solution for data visualization;To put it simply - SQL helps us store and manipulate the data we are working with, Python allows us to write code and perform calculations, and then Tableau enables beautiful data visualization. A well-thought-out integration stepping on these three pillars could save a business millions of dollars annually in terms of reporting personnel.Therefore, it goes without saying that employers are looking for Python, SQL, and Tableau when posting Data Scientist and Business Intelligence Analyst job descriptions. Not only that, but they would want to find a candidate who knows how to use these three tools simultaneously. This is how recurring data analysis tasks can be automated.So, in this course we will to teach you how to integrate Python, SQL, and Tableau. An essential skill that would give you an edge over other candidates. In fact, the best way to differentiate your job resume and get called for interviews is to acquire relevant skills other candidates lack. And because, we have prepared a topic that hasn't been addressed elsewhere, you will be picking up a skill that truly has the potential to differentiate your profile.Many people know how to write some code in Python.Others use SQL and Tableau to a certain extent.Very few, however, are able to see the full picture and integrate Python, SQL, and Tableau providing a holistic solution. In the near future, most businesses will automate their reporting and business analysis tasks by implementing the techniques you will see in this course. It would be invaluable for your future career at a corporation or as a consultant, if you end up being the person automating such tasks.Our experience in one of the large global companies showed us that a consultant with these skills could charge a four-figure amount per hour. And the company was happy to pay that money because the end-product led to significant efficiencies in the long run.The course starts off by introducing software integration as a concept. We will discuss some important terms such as servers, clients, requests, and responses. Moreover, you will learn about data connectivity, APIs, and endpoints.Then, we will continue by introducing the real-life example exercise the course is centered around - the 'Absenteeism at Work' dataset. The preprocessing part that follows will give you a taste of how BI and data science look like in real-life on the job situations. This is extremely important because a significant amount of a data scientist's work consists in preprocessing, but many learning materials omit that Then we would continue by applying some Machine Learning on our data. You will learn how to explore the problem at hand from a machine learning perspective, how to create targets, what kind of statistical preprocessing is necessary for this part of the exercise, how to train a Machine Learning model, and how to test it. A truly comprehensive ML exercise.Connecting Python and SQL is not immediate. We have shown how that's done in an entire section of the course. By the end of that section, you will be able to transfer data from Jupyter to Workbench.And finally, as promised, Tableau will allow us to visualize the data we have been working with. We will prepare several insightful charts and will interpret the results together.As you can see, this is a truly comprehensive data science exercise. There is no need to think twice. If you take this course now, you will acquire invaluable skills that will help you stand out from the rest of the candidates competing for a job.Also, we are happy to offer a 30-day unconditional no-questions-asked-money-back-in-full guarantee that you will enjoy the course.So, let's do this! The only regret you will have is that you didn't find this course sooner! Overview Section 1: Introduction Lecture 1 What Does the Course Cover? Section 2: What is software integration? Lecture 2 Properties and Definitions: Data, Servers, Clients, Requests and Responses Lecture 3 Properties and Definitions: Data Connectivity, APIs, and Endpoints Lecture 4 Further Details on APIs Lecture 5 Text Files as Means of Communication Lecture 6 Definitions and Applications Section 3: Setting up the working environment Lecture 7 Setting Up the Environment - An Introduction (Do Not Skip, Please)! Lecture 8 Why Python and why Jupyter? Lecture 9 Installing Anaconda Lecture 10 The Jupyter Dashboard - Part 1 Lecture 11 The Jupyter Dashboard - Part 2 Lecture 12 Jupyter Shortcuts Lecture 13 Installing sklearn Lecture 14 Installing Packages - Exercise Lecture 15 Installing Packages - Solution Section 4: What's next in the course? Lecture 16 Up Ahead Lecture 17 Real-Life Example: Absenteeism at Work Lecture 18 Real-Life Example: The Dataset Lecture 19 Important Notice Regarding Datasets Section 5: Preprocessing Lecture 20 What to Expect from the Next Couple of Sections Lecture 21 Data Sets in Python Lecture 22 Data at a Glance Lecture 23 A Note on Our Usage of Terms with Multiple Meanings Lecture 24 ARTICLE - A Brief Overview of Regression Analysis Lecture 25 Picking the Appropriate Approach for the Task at Hand Lecture 26 Removing Irrelevant Data Lecture 27 EXERCISE - Removing Irrelevant Data Lecture 28 SOLUTION - Removing Irrelevant Data Lecture 29 Examining the Reasons for Absence Lecture 30 Splitting a Column into Multiple Dummies Lecture 31 EXERCISE - Splitting a Column into Multiple Dummies Lecture 32 SOLUTION - Splitting a Column into Multiple Dummies Lecture 33 ARTICLE - Dummy Variables: Reasoning Lecture 34 Dummy Variables and Their Statistical Importance Lecture 35 Grouping - Transforming Dummy Variables into Categorical Variables Lecture 36 Concatenating Columns in Python Lecture 37 EXERCISE - Concatenating Columns in Python Lecture 38 SOLUTION - Concatenating Columns in Python Lecture 39 Changing Column Order in Pandas DataFrame Lecture 40 EXERCISE - Changing Column Order in Pandas DataFrame Lecture 41 SOLUTION - Changing Column Order in Pandas DataFrame Lecture 42 Implementing Checkpoints in Coding Lecture 43 EXERCISE - Implementing Checkpoints in Coding Lecture 44 SOLUTION - Implementing Checkpoint in Coding Lecture 45 Exploring the Initial "Date" Column Lecture 46 Using the "Date" Column to Extract the Appropriate Month Value Lecture 47 Introducing "Day of the Week" Lecture 48 EXERCISE - Removing Columns Lecture 49 Further Analysis of the DataFrame: Next 5 Columns Lecture 50 Further Analysis of the DaraFrame: "Education", "Children", "Pets" Lecture 51 A Final Note on Preprocessing Lecture 52 A Note on Exporting Your Data as a *.csv File Section 6: Machine Learning Lecture 53 Exploring the Problem from a Machine Learning Point of View Lecture 54 Creating the Targets for the Logistic Regression Lecture 55 Selecting the Inputs Lecture 56 A Bit of Statistical Preprocessing Lecture 57 Train-test Split of the Data Lecture 58 Training the Model and Assessing its Accuracy Lecture 59 Extracting the Intercept and Coefficients from a Logistic Regression Lecture 60 Interpreting the Logistic Regression Coefficients Lecture 61 Omitting the dummy variables from the Standardization Lecture 62 Interpreting the Important Predictors Lecture 63 Simplifying the Model (Backward Elimination) Lecture 64 Testing the Machine Learning Model Lecture 65 How to Save the Machine Learning Model and Prepare it for Future Deployment Lecture 66 ARTICLE - More about 'pickling' Lecture 67 EXERCISE - Saving the Model (and Scaler) Lecture 68 Creating a Module for Later Use of the Model Section 7: Installing MySQL and Getting Acquainted with the Interface Lecture 69 Installing MySQL Lecture 70 Installing MySQL on macOS and Unix systems Lecture 71 Setting Up a Connection Lecture 72 Introduction to the MySQL Interface Section 8: Connecting Python and SQL Lecture 73 Are you sure you're all set? Lecture 74 Implementing the 'absenteeism_module' - Part I Lecture 75 Implementing the 'absenteeism_module' - Part II Lecture 76 Creating a Database in MySQL Lecture 77 Importing and Installing 'pymysql' Lecture 78 Creating a Connection and Cursor Lecture 79 EXERCISE - Create 'df_new_obs' Lecture 80 Creating the 'predicted_outputs' table in MySQL Lecture 81 Running an SQL SELECT Statement from Python Lecture 82 Transferring Data from Jupyter to Workbench - Part I Lecture 83 Transferring Data from Jupyter to Workbench - Part II Lecture 84 Transferring Data from Jupyter to Workbench - Part III Section 9: Analyzing the Obtained data in Tableau Lecture 85 EXERCISE - Age vs Probability Lecture 86 Analysis in Tableau: Age vs Probability Lecture 87 EXERCISE - Reasons vs Probability Lecture 88 Analysis in Tableau: Reasons vs Probability Lecture 89 EXERCISE - Transportation Expense vs Probability Lecture 90 Analysis in Tableau: Transportation Expense vs Probability Section 10: Bonus lecture Lecture 91 Bonus Lecture: Next Steps Intermediate and advanced students,Students eager to differentiate their resume,Individuals interested in a career in Business Intelligence and Data Science Homepage https://www.udemy.com/course/python-sql-tableau-integrating-python-sql-and-tableau/ Download ( Rapidgator ) https://rapidgator.net/file/298853203884e2fb995099c326fb69a4/kbsbv.Python..Sql..Tableau.Integrating.Python.Sql.And.Tableau.part1.rar.html https://rapidgator.net/file/a7dffb09e669c5e3f57af3c198813e67/kbsbv.Python..Sql..Tableau.Integrating.Python.Sql.And.Tableau.part2.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/5489D0052c3baC28/kbsbv.Python..Sql..Tableau.Integrating.Python.Sql.And.Tableau.part2.rar https://uploadgig.com/file/download/955CEe3616d76a3C/kbsbv.Python..Sql..Tableau.Integrating.Python.Sql.And.Tableau.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/10509C13CEA786D/kbsbv.Python..Sql..Tableau.Integrating.Python.Sql.And.Tableau.part1.rar https://nitroflare.com/view/56FD39EBF464ADD/kbsbv.Python..Sql..Tableau.Integrating.Python.Sql.And.Tableau.part2.rar Links are Interchangeable - No Password - Single Extraction
  12. Duration: 8h 10m | Video: 1280x720 30fps | Audio: AAC, 48 kHz, 2ch | Size: 4.35 GB Genre: eLearning | Language: English Modern Python LiveLessons: Big Ideas and Little Code in Python provides developers with an approach to programming in Python that expresses big ideas succinctly, with the minimum of code, allowing the business logic to shine through. It does so using a number of relevant examples from current problems, including data analytics and social media. Description In this video training, Raymond Hettinger starts by introducing modern Python foundational skills, tools, and techniques in the first half of the lessons. In the second part he shows you how to apply the tools and techniques to a real application. About the Instructor Raymond Hettinger has been a Python Core Developer since 2001 and received the Python Software Foundation Distinguished Service Award in 2014. Currently, he runs an international Python training and consulting business. He is the author of many parts of Python, including itertools, collections, sets, soerted, enumerate, and reversed. Skill Level Intermediate What You Will Learn Core skills of modern Python that enable you to elegantly code powerful solutions succinctly and efficiently How to use continuous and discreet functions in the random module, collections.Counter(), lambda, list operations, chained comparisons, and f-strings How to use random.choice() and random.sample(); do resampling, bootstrapping, and significance testing; and run simulations How to run static analysis on code with type hints and use static type checking How to use defaultdict for grouping, key functions for data ordering, and zip* to transpose data, and how to flatten 2D data with multiple loops and list comprehension How to use k-means to implement unsupervised learning More defaultdict skills with which to pivot and accumulate data and reverse a one-to-many mapping How to use sorted, bisect, and merge and how to conserve memory with string interning How to normalize text and use the hashing tools in hashlib How to use Bottle to build REST APIs and web applications How to test using pytest, itertools, Hypothesis, pyflakes, mypy, and data validators Who Should Take This Course Developers looking to improve their modern Pythons skills Course Requirements Basic understanding of programming and development Familiarity with the Python language https://www.informit.com/store/modern-python-livelessons-big-ideas-and-little-code-9780134743417 Download ( Rapidgator ) https://rapidgator.net/file/234da0e9c371689b08ae2ebc574139fe/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part1.rar.html https://rapidgator.net/file/37bf31775e24baf77a3efc38af06825c/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part3.rar.html https://rapidgator.net/file/9dc8675398ebaaa82424f398e2a3ee71/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part2.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/53da09Dd2d2C4f7e/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part2.rar https://uploadgig.com/file/download/9DDb64a069093639/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part3.rar https://uploadgig.com/file/download/fD6449c2274a7b62/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/39CA9A2024384D9/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part3.rar https://nitroflare.com/view/5CD34C439EF2FD8/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part1.rar https://nitroflare.com/view/E154CC87982ABC6/zichj...Modern.Python.Big.Ideas.and.Little.Code.in.Python.part2.rar Links are Interchangeable - No Password - Single Extraction
  13. Released 07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + vtt | Duration: 1h 36m | Size: 216.1 MB This course will teach you how to apply and reason about Python's support for abstract base classes and virtual inheritance Abstract base classes assist in many different areas within Python. In this course, Core Python: Abstract Base Classes, you'll learn to work with and design programs using Python support for abstract base classes. First, you'll explore the concept of abstract base classes along with Python's core support for it. Next, you'll discover parts of Python's standard library that help you work with abstract base classes. Finally, you'll learn how to use abstract methods in your class designs. When you're finished with this course, you'll have the skills and knowledge of abstract classes and virtual inheritance needed to apply them in your designs and work with code that uses them. Homepage https://www.pluralsight.com/courses/core-python-abstract-base-classes Download ( Rapidgator ) https://rapidgator.net/file/62aff35c205c2ed41152da0ce70dc439/zvtnm.Core.Python.Abstract.Base.Classes.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/1ff9e3a68b30e1F0/zvtnm.Core.Python.Abstract.Base.Classes.rar Download ( NitroFlare ) https://nitroflare.com/view/D2B3036395802BD/zvtnm.Core.Python.Abstract.Base.Classes.rar Links are Interchangeable - No Password - Single Extraction
  14. Last updated 6/2021 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.22 GB | Duration: 6h 0m Create a Python App and Connect to and interact with Databases : PostgreSQL | MySQL |SQL Server | Oracle|SQLite What you'll learn Install and setup multiple database systems Create virtual environments Install database connector drivers for multiple database systems Create Python Application GUI Frontend Create database and table in multiple database systems Create a class and methods in Python File Import and use Python Modules Create database connection configuration file Interact with databases from Python Application GUI Perform various database operations from Python App GUI Requirements Basic Knowledge of SQL Basic knowledge of Python Description In this course you will learn how to create a Python application program that will interact with the following database systems: SQL Server databasePostgreSQLMySQLOracleSQLitePython has various modules that you can use to interact with databases. We will install the various database connector module for each database system to enable us interact with the various database systems from our Python application.We will setup the various database management systems and also create a new database and table that our Python application will interact with.We will create the Python application frontend using Tkinter which is a built in Python module used to create graphical user interfaces . From tkinter module we will import ttk module which is a themed widget library that contains various types of widgets like buttons,labels etc that we can use to build the graphical user interface for our Python application. We will also import other bits and pieces from the tkinter module that our Python application will use.Also we will create a database configuration file that twill be used to interact with the various database systems that we will connect to from our Python application. We will perform various database operations on the databases we will create from out Python application GUI frontend. Overview Section 1: Creating the application user interface Lecture 1 Introduction Lecture 2 What is CRUD Lecture 3 What we will create Lecture 4 Application Design Sketch Lecture 5 Install Python on Windows Lecture 6 Install Python3 on Mac Lecture 7 Install Atom Text Editor Lecture 8 Create project directory and Python File Lecture 9 Creating the GUI : Part 1 Lecture 10 Creating the GUI : Part 2 Lecture 11 Creating the GUI : Part 3 Lecture 12 Creating the GUI : Part 4 Lecture 13 Creating the GUI : Part 5 Lecture 14 Add comments to your code Section 2: Setting Up Microsoft SQL Server Database Lecture 15 Installing SQL Server 2017 Express Lecture 16 The Source Code Lecture 17 Create a new database and table Lecture 18 Create a database configuration file Lecture 19 Create a virtual environment and install pypyodbc Lecture 20 Connect Python File to database Lecture 21 Create a cursor object Lecture 22 Create a class and methods Lecture 23 Add more methods Lecture 24 Create a function for selected rows Lecture 25 Create more functions Lecture 26 Activate button widgets Lecture 27 Interacting with database from Python Application GUI - Part 1 Lecture 28 Interacting with database from Python Application GUI - Part 2 Section 3: Setting Up MySQL Database Server Lecture 29 Installing MySQL Lecture 30 MySQL Workbench Lecture 31 Create a database and a table Lecture 32 Create a configuration connection file Lecture 33 Install Mysql -Connector in a virtual environment Lecture 34 Modfify Python Application File to connect to Mysql Database Lecture 35 Test connection to MySQL Database from Python App GUI Lecture 36 Interacting with MySQL database from Python Application GUI - Part 1 Lecture 37 Interacting with MySQL database from Python Application GUI - Part 2 Lecture 38 The Source Code Section 4: Setting Up PostgreSQL Database Server Lecture 39 Installing PostgreSQL Lecture 40 Create a database and a table Lecture 41 Create a sequence for the id column Lecture 42 Create a configuration connection file Lecture 43 Install Pyscopg2 inside a virtual environment Lecture 44 Modfify Python Application File to connect to PostgreSQL Database Lecture 45 Test connection to PostgreSQL Database from Python App GUI Lecture 46 Interacting with PostgreSQL database from Python Application GUI - Part 1 Lecture 47 Interacting with PostgreSQL database from Python Application GUI - Part 2 Lecture 48 The Source Code Section 5: Setting Up Oracle Database Server Lecture 49 Download and Install Oracle Database 18c Express Edition Lecture 50 What is SQLPLUS Lecture 51 Unlock sample HR Schema Account Lecture 52 Install Oracle SQL Developer Lecture 53 Create a new database table Lecture 54 Connect Oracle developer to Oracle database Lecture 55 Installing Oracle database driver in a virtual environment Lecture 56 Modfify Python Application File to connect to Oracle Database Lecture 57 Interacting with Oracle database from Python Application GUI Lecture 58 The Source Code Section 6: Setting Up SQLite3 Database Lecture 59 Installing SQLite Lecture 60 Modfify Python Application File to connect to SQLite3 Database Lecture 61 Test connection to SQLite3 Database from Python App GUI Lecture 62 Interacting with SQLite3 database from Python Application GUI Lecture 63 SQLiteStudio Lecture 64 DB Browser for SQLite Lecture 65 Viewing SQlite3 Database Records Lecture 66 The Source Code Lecture 67 Thank You Beginners to Python,Beginners to Programming Homepage https://www.udemy.com/course/create-a-python-application-to-connect-to-multiple-databases/ Download ( Rapidgator ) https://rapidgator.net/file/81e5a4dcff0ab427ec3b3d2c303e4a8b/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part1.rar.html https://rapidgator.net/file/a81b5a6b216d1940c04cdc517a41a859/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part2.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/1986f5989f0fCA2b/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part2.rar https://uploadgig.com/file/download/229628829fb60D29/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/417C4D4EC218B90/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part1.rar https://nitroflare.com/view/BA5B492CC87C5B9/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part2.rar Links are Interchangeable - No Password - Single Extraction
  15. Last updated 6/2021 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.22 GB | Duration: 6h 0m Create a Python App and Connect to and interact with Databases : PostgreSQL | MySQL |SQL Server | Oracle|SQLite What you'll learn Install and setup multiple database systems Create virtual environments Install database connector drivers for multiple database systems Create Python Application GUI Frontend Create database and table in multiple database systems Create a class and methods in Python File Import and use Python Modules Create database connection configuration file Interact with databases from Python Application GUI Perform various database operations from Python App GUI Requirements Basic Knowledge of SQL Basic knowledge of Python Description In this course you will learn how to create a Python application program that will interact with the following database systems: SQL Server databasePostgreSQLMySQLOracleSQLitePython has various modules that you can use to interact with databases. We will install the various database connector module for each database system to enable us interact with the various database systems from our Python application.We will setup the various database management systems and also create a new database and table that our Python application will interact with.We will create the Python application frontend using Tkinter which is a built in Python module used to create graphical user interfaces . From tkinter module we will import ttk module which is a themed widget library that contains various types of widgets like buttons,labels etc that we can use to build the graphical user interface for our Python application. We will also import other bits and pieces from the tkinter module that our Python application will use.Also we will create a database configuration file that twill be used to interact with the various database systems that we will connect to from our Python application. We will perform various database operations on the databases we will create from out Python application GUI frontend. Overview Section 1: Creating the application user interface Lecture 1 Introduction Lecture 2 What is CRUD Lecture 3 What we will create Lecture 4 Application Design Sketch Lecture 5 Install Python on Windows Lecture 6 Install Python3 on Mac Lecture 7 Install Atom Text Editor Lecture 8 Create project directory and Python File Lecture 9 Creating the GUI : Part 1 Lecture 10 Creating the GUI : Part 2 Lecture 11 Creating the GUI : Part 3 Lecture 12 Creating the GUI : Part 4 Lecture 13 Creating the GUI : Part 5 Lecture 14 Add comments to your code Section 2: Setting Up Microsoft SQL Server Database Lecture 15 Installing SQL Server 2017 Express Lecture 16 The Source Code Lecture 17 Create a new database and table Lecture 18 Create a database configuration file Lecture 19 Create a virtual environment and install pypyodbc Lecture 20 Connect Python File to database Lecture 21 Create a cursor object Lecture 22 Create a class and methods Lecture 23 Add more methods Lecture 24 Create a function for selected rows Lecture 25 Create more functions Lecture 26 Activate button widgets Lecture 27 Interacting with database from Python Application GUI - Part 1 Lecture 28 Interacting with database from Python Application GUI - Part 2 Section 3: Setting Up MySQL Database Server Lecture 29 Installing MySQL Lecture 30 MySQL Workbench Lecture 31 Create a database and a table Lecture 32 Create a configuration connection file Lecture 33 Install Mysql -Connector in a virtual environment Lecture 34 Modfify Python Application File to connect to Mysql Database Lecture 35 Test connection to MySQL Database from Python App GUI Lecture 36 Interacting with MySQL database from Python Application GUI - Part 1 Lecture 37 Interacting with MySQL database from Python Application GUI - Part 2 Lecture 38 The Source Code Section 4: Setting Up PostgreSQL Database Server Lecture 39 Installing PostgreSQL Lecture 40 Create a database and a table Lecture 41 Create a sequence for the id column Lecture 42 Create a configuration connection file Lecture 43 Install Pyscopg2 inside a virtual environment Lecture 44 Modfify Python Application File to connect to PostgreSQL Database Lecture 45 Test connection to PostgreSQL Database from Python App GUI Lecture 46 Interacting with PostgreSQL database from Python Application GUI - Part 1 Lecture 47 Interacting with PostgreSQL database from Python Application GUI - Part 2 Lecture 48 The Source Code Section 5: Setting Up Oracle Database Server Lecture 49 Download and Install Oracle Database 18c Express Edition Lecture 50 What is SQLPLUS Lecture 51 Unlock sample HR Schema Account Lecture 52 Install Oracle SQL Developer Lecture 53 Create a new database table Lecture 54 Connect Oracle developer to Oracle database Lecture 55 Installing Oracle database driver in a virtual environment Lecture 56 Modfify Python Application File to connect to Oracle Database Lecture 57 Interacting with Oracle database from Python Application GUI Lecture 58 The Source Code Section 6: Setting Up SQLite3 Database Lecture 59 Installing SQLite Lecture 60 Modfify Python Application File to connect to SQLite3 Database Lecture 61 Test connection to SQLite3 Database from Python App GUI Lecture 62 Interacting with SQLite3 database from Python Application GUI Lecture 63 SQLiteStudio Lecture 64 DB Browser for SQLite Lecture 65 Viewing SQlite3 Database Records Lecture 66 The Source Code Lecture 67 Thank You Beginners to Python,Beginners to Programming Homepage https://www.udemy.com/course/create-a-python-application-to-connect-to-multiple-databases/ Download ( Rapidgator ) https://rapidgator.net/file/81e5a4dcff0ab427ec3b3d2c303e4a8b/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part1.rar.html https://rapidgator.net/file/a81b5a6b216d1940c04cdc517a41a859/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part2.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/1986f5989f0fCA2b/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part2.rar https://uploadgig.com/file/download/229628829fb60D29/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part1.rar Download ( NitroFlare ) https://nitroflare.com/view/417C4D4EC218B90/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part1.rar https://nitroflare.com/view/BA5B492CC87C5B9/byuxi.Create.A.Python.Application.To.Connect.To.Multiple.Databases.part2.rar Links are Interchangeable - No Password - Single Extraction
  16. Published 07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 71 lectures (10h 29m) | Size: 3.73 GB Learn Django web development | Theory and hands-on demonstrations | Deploy a Django Web Application What you'll learn How to build a Django website from scratch How to deploy our website and host it on a live server Build and connect a database User registration User authentication - (login and logout) Build a real-world application that helps you to manage all of your ideas and thoughts How to perform the basic CRUD (Create, Read, Update, Delete) operations How to manage static files How to create a virtual environment Password management and email handling Learn the concept of apps Learn about views, urls and templates How to configure Amazon S3 buckets and Amazon RDS (PostgreSQL database) Requirements A basic understanding of HTML & CSS, JavaScript and Python. Some knowledge of web development would be helpful, but it is not mandatory. Description Welcome! I'm here to help you to master the basics of Django web development. -- > COURSE LAUNCHED IN JULY 2022 This course is primarily intended for beginners who have no experience with Django. Although, there may be topics covered in this course that intermediate Django developers might be unaware of and may wish to learn so that they can add to their pre-existing knowledge. To put it simply, if you have a background in python and want to take your first step into learning web development and how to deploy a live website in the end, then this course is for you! I want to help YOU master the basics of Django. The Python Django: Ultimate Beginners Course is a unique course because of its high value at low cost, simplicity, and attention to detail. Everything that has been designed from the styling to the graphics and topics covered is crafted with the absolute duty of care towards the student. This makes it very different from similar courses that you'll find here on Udemy. It covers all the basic topics that a new beginner to Django is expected to know and be aware of. It is mixed with theory and practical hands-on demonstrations. The course is structured in a logical and cohesive way - not just random slides plastered everywhere. It starts off very simple and then builds on gradually throughout the course. You will also learn 3 valuable AWS services: Identity and Access Management (IAM), Amazon S3, and the Relational Database System (RDS). This course is jam-packed with lecture slides, PDF walkthroughs, code snippets/references and comes along with the full project source code - as a zip file. All 265 + slides are available as a downloadable PDF. The Python Django: Ultimate Beginners Course is a highly practical course and allows you to apply your knowledge There is a wealth of hands-on lectures throughout this course. Not only will you be learning how to code in Django, but you will be utilizing cloud platforms, such as AWS and Heroku - whilst we use the free tier. --------------------------------------------------- Your instructor My name is Arno Pretorius. I'm a qualified IT teacher who has taught programming both in-person and online. My main passions are teaching and technology, so I thought why not just combine the best of both worlds to create something truly amazing and valuable. Over the years, I have created and deployed many real-world Django-based applications, including a job portal for university graduates and an exclusive social network. I'm a Django developer, an AWS Certified Cloud Practitioner, Solutions Architect, and Developer. I have a keen interest in everything that relates to web development, Django web application security, and cloud computing. I'm also an Amazon author and have published books on Django web application security and basic web development. So, let's go and become fluent in Django, along with an extra service or two. Trust me you are in good hands! --------------------------------------------------- This course also comes with - 10.5 hours of on-demand video - Full lifetime access - A Udemy certificate of completion - 11 downloadable resources - Access on mobile and TV - A 30-Day "No Questions Asked" Money Back Guarantee! Join me in this course if you want to master the basics of Django and kick-start your career in web development! Who this course is for This course is primarily intended for beginners who have no experience with Django. Although, there may be topics covered in this course which intermediate Django developers might be unaware of and may wish to learn so that they can add to their pre-existing knowledge. We will be learning everything from scratch and cover all the basics along with a few additional topics and principles which will prove useful to every skill level. To put it simply, if you have a background in python and want to take your first step into learning web development and how to deploy a live website in the end, then this course is for you! Homepage https://www.udemy.com/course/python-django-ultimate-beginners-course-2022/ Download ( Rapidgator ) https://rapidgator.net/file/bdf25d69dedcc11eb57e826d9a4769ad/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part1.rar.html https://rapidgator.net/file/f9e657fb5e1e307e93974a1250dd5f7c/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part2.rar.html https://rapidgator.net/file/b5dde286f7709c559c6d1f7188283c3e/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part3.rar.html https://rapidgator.net/file/5018d7653be0ce3aa100493d8d7553dd/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part4.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/d86cb5Da205ba6b3/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part1.rar https://uploadgig.com/file/download/CaAcD880a7B64fE4/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part2.rar https://uploadgig.com/file/download/8BEdd7dbc177637a/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part3.rar https://uploadgig.com/file/download/925b86EeE5c8a564/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part4.rar Download ( NitroFlare ) https://nitroflare.com/view/FAC7E0CCB107BC0/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part1.rar https://nitroflare.com/view/75C1659A17C813A/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part2.rar https://nitroflare.com/view/95159E2FB3E2BF9/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part3.rar https://nitroflare.com/view/ECE4137AE3AAE43/zpniv.Python.Django.Ultimate.Beginners.Course..2022.part4.rar Links are Interchangeable - No Password - Single Extraction
  17. Published 07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration:22 lectures (9h 2m) | Size: 4 GB An In-depth practical guide to manual unit and integration testing in Python with real-world case scenarios. What you'll learn Analyze existing code bases (& refactor them if needed), and design the needed test cases accordingly. Perform manual unit and integration tests on Procedural code and Object Oriented code. Learn how to perform Test Driven Development (TDD) Perform test coverage and run multiple tests simultaneously. Learn how to deal with boiler-plate code and pass-through methods/functions. Utilize Pdb (Interactive Python Debugger) on run-time to inspect different test cases. Learn how to effectively create mocks and when to do it based on various case scenarios, Know how to fake/sample real-world data. Gain a sneak-peak on best practices in OOA, OOD & OOP by implementing UML design schemes from scratch and write tests for the code base you created later on. Requirements Fair background in Python is a must. Fair background in OOP in Python (specifically classes & object creation, classmethods, staticmethods, getters & setters). (don't worry I take everything gradually in this course) If you felt anything that you lack or do not understand, reach out to me, I will be available 24/7. If you think you need more background on any of the side topics illustrated, you can find exactly the materials to get you started in my other courses. Description Looking for a beginner-friendly, 'getting-started' guide, that happens to ALSO be as comprehensive as possible with rich real-world case scenarios that COMPLETELY covers the all aspects, the ins-and-outs, the nits and grits of Unit and Integration testing Python? Yes dear, you are at the right place. Welcome to "Hands-On Software Testing in Python" ! If you are a freelancer, a college student, or a software engineer, at some point it's just inevitable to not run atleast some 'exploratory testing' scripts to inspect and test various edge cases, just to make sure how solid your work is, but things starts to go downhill when a certain component breaks down, it's either you go into a refactoring purgatory, or you start the project again from scratch! In this course I will show you multiple case scenarios, where we will have existing code bases, as well as mini-projects that we will build from scratch in a code-along fashion, what matters the most is that we will follow a systematic procedure to analyze and design our test cases, for each case scenario, and then implement them. My name is Ahmed Alhallag, I'm a Software Engineer and an Assistant Lecturer. I will take you through an intensive journey starting with the theoretical concepts behind Software Testing, up to the inner-most parts where you will learn the best practices in approaching any project, designed and implemented in any paradigm (Procedural & OOP are covered in this course), no matter how large or big it might seem from the surface. Writing code isn't supposed to be the main point of focus for you as a software developer, especially code that just 'works' for a current point of time. A bunch of other factors needs to be taken into consideration such as Quality of modeling You might think: "alright, I have this task to implement, so let's just wing it!", and you immediately hop on to your favorite IDE and starting coding. For certain tasks, maybe this would be the time-efficient thing to do, for larger one, this will eventually consume your time on the long run, because you will keep going back in forth in encapsulating this block of code, and refactoring that block of code, saving your sensitive data in a .env file after it was thrown away at the top of your script when you started working, serializing this output, and persisting that output into a json file instead of a basic txt file, creating a middleware, an API interface or a certain controller that you suddenly found out that you need to perform some action, implement a data handler because the code became so redundant and intertwined, changing up the entire set of data structures used, which as a result, changes the entire processing logic written ahead of time as well! See, all of the previous scenarios, are just daily events that we go through in our development journey, if you haven't faced any of them yet, that's good news! We will briefly cover a systematic way to implement our code via analysis (OOA) and design (OOD using UML), with the help of mini-projects that we will build together! This is obviously not the focus of this course, so we won't be spending much time on that part, we will take we need to get to have a clean code base later on when we implement. Quality of code After the brief concepts on conceptual designs and modeling, we will follow the guides (class diagrams) we created to implement the core system we will test bit by bit later on. We will be using Object Oriented Programming (OOP) for the majority of our implementations. You might ask yourself, why would we spend THIS much time at the implementation of every use case? why not just take the code as it is and start writing test cases? That's a valid point, but hear me out: I believe that if your want to know the ins and outs of a system, whether you are performing a defensive/offensive security measure, designing a UI/UX, implementing a database layer, or executing some test cases, you need to be FULLY and THOROUGHLY aware of the system you are working on. For experienced developers, they can definitely pick the pace off at any point in time and start implementing their test cases, but for anyone who has not written a lot of test cases before, or at the start of their journey, this would be a hassle! not knowing what exactly that needs to be tested, or even not knowing how or where to start! This is the basis of the approach I'm following in most of the given case scenarios, where basically we will build the end-system we would want to thoroughly test from scratch! After having a brief on best practices in code/system modeling, and implementing these systems manually, we will dive deep into testing, where We will mainly use the unittest framework in Python, which is a built-in easy to use module to get started as quickly as possible. We will start implementing the mini-project in a Procedural manner (using the most basic modularization approach; functions). We will then how to design and implement Unit Tests for our procedural code, by setting up our terms of agreement on what exactly should be defined as a "Unit", as well as configuring of new project hierarchy (tree of folders). We will have a walkthrough into one of the most common approaches used for testing; Test Driven Development, or TDD for short. We will then have a Paradigm shift, where we travel to the OOP realm. I'm assuming you have a fair background in OOP, a brief recap will be given. (watch the promotional/intro video) We will write Unit Tests for Object Oriented (classes) that we have created, and an update to our "unit" definition will take place. We will see how useful your knowledge of the Command Line Interface (CLI) can be, by using your terminal session to inspect and debug your unit tests in real-time using the Python Interactive Debugger. We will have real data, and we will learn how to sample them and/or fake them when needed We will design and implement interrelated modules, we will also design and implement the relationships between them. And will know that by doing so, another different added layer/type of testing needs to be performed, which is Integration Testing, to make sure that integrated components, work as intended. We will see a lot of coupled dependencies, which will lead us to isolate them completely using "Mocking" to perform our unit tests. We will learn how to deal with intermediary or pass-through code, as it sometimes becomes unclear to whether you should test them or not. We will learn how to generate test coverage reports (in HTML) and how to run multiple (unit and integration) tests simultaneously. Finally, we will have an intensive walkthrough on files handlers and operations (such read, write, etc..) as well as the common stdout and stdout functions (such as print, input) and how to exactly test them for full coverage. Who this course is for Python developers who have some background in Python and want to dive in the software testing world. CS/CE students in their first or second year who just got to read about software testing and TDD. Homepage https://www.udemy.com/course/hands-on-software-testing-in-python-w-unittest-framework/ Download ( Rapidgator ) https://rapidgator.net/file/27b468dce3b4851c3f83b5923fcd7925/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part1.rar.html https://rapidgator.net/file/5c9ca7168d8d6086ec1384366ef79b1d/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part2.rar.html https://rapidgator.net/file/912b61e92a686a3d934c9589f9a223e2/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part3.rar.html https://rapidgator.net/file/1bab4a3a1657a404d05c71df070934ac/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part4.rar.html https://rapidgator.net/file/8ae744521a4780a811b0e8dd50407ab3/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part5.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/a206CC4f1b5cf968/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part1.rar https://uploadgig.com/file/download/8013862b88506D75/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part2.rar https://uploadgig.com/file/download/5953fe3442cCbd85/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part3.rar https://uploadgig.com/file/download/177fa2ad14D023Ae/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part4.rar https://uploadgig.com/file/download/Ed9ca980Df341752/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part5.rar Download ( NitroFlare ) https://nitroflare.com/view/A4E2B189A9EDD8B/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part1.rar https://nitroflare.com/view/31E6318C6AAD63A/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part2.rar https://nitroflare.com/view/70BA8280DC49F0A/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part3.rar https://nitroflare.com/view/DD7B5C06368FFF3/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part4.rar https://nitroflare.com/view/A3F558E37DE45B4/jfivc.HandsOn.Software.Testing.In.Python.w.unittest.framework.part5.rar Links are Interchangeable - No Password - Single Extraction
  18. Duration: 52m | Video: .MP4, 1280x720, 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 436 MB Genre: eLearning | Language: English In this course you'll get the skills to automate Excel with the Power of Python! Using OpenPyXL module you will learn how to read and write Excel Files, work with sheets and styles. Since this is intermediate Python you are required to already master the basics of Python before enrolling into this class. My advice is to first check my other classes on Python published here on SkillShare; they will help you build a strong foundation of Python Programming Language. In this course we'll get the skills to get ahead! Major topics of this Python and Excel course Setup the Environment. Installing OpenPyXL Excel Basics Reading Excel Documents Reading Data in a Cell Range Writing Excel Files Creating New Excel Files Using Excel Formulas Sheets Operations Working with Styles and more! Excel file used in the course:https://drive.google.com/open?id=1NANQ_sVqWK1d-JvE9B3Zd9ZKxpQxAG8K Homepage https://www.skillshare.com/classes/Automate-Excel-with-Python-using-OpenPyXL/196256383 Download from UploadCloud https://www.uploadcloud.pro/7lnx8iz2t746/sakpg.A.E.w.P.u.O.rar.html Download ( Rapidgator ) https://rapidgator.net/file/da4b9dcf483f020f41f1cd7617d52664/sakpg.A.E.w.P.u.O.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/Ef86Bc645e59B1d5/sakpg.A.E.w.P.u.O.rar Download ( NitroFlare ) https://nitroflare.com/view/ADBCDCD5B8122D3/sakpg.A.E.w.P.u.O.rar Links are Interchangeable - No Password - Single Extraction
  19. Published 7/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.66 GB | Duration: 4h 16m Learn to develop the graphical user interface of the application using PySide What you'll learn Learn working with Pyside and achieve expertise in developing the cross-platform GUI by following the Qt standard This course will also help the trainees to dig into some of the advanced level concepts of Python which revolve around GUI development. It is intended to make the trainees expert on Pyside and Qt libraries. Trainees will learn every single topic that falls under the court of Pyside. After completing this course, the trainees will be able to support the project on python that requires cross-platform GUI development. Requirements To learn Pyside, there are some of the concepts or technologies you should have a basic understanding of to learn this. The very first thing is the basics of python. As PySide is entirely based on python, you just need a basic understanding of the Python programming language If you are hands-on in python, you will be able to focus on learning the complex concepts rather than struggling to learn the basics. The second thing is the Libraries. If you are having an idea about how the libraries are built and used, you will find it very easy to learn the concepts where different libraries will be used. These prerequisites have been included at the beginning of the course so that you can find everything in one place. However, it is always recommended that trainees should be having a basic understanding of these concepts before Description To work with PySide, there are some concepts that one must master. In this course, we have ensured to cover all such concepts that we want our trainees to become expert in so that they can work effectively with Pyside. At the beginning of this PySide training, the trainees will get to learn about how PySide helps in solving the business problems and will also learn how to determine if the solution could be drafted using it. Later in the course, they will be covering the advanced level concepts which will help them to dive deep into the core concepts that are used when there is an urge to bring some complex level functionalities in the application. We have also added two projects in this course where the project's topics have been decided in a way so that it covers all the topics that they would have walked through in this course. Trainees will also learn about error handling in this course which will help them while they will need to implement things while developing the critical applicationPyside can be defined as the python extension that facilitates application development by allowing access to Qt libraries. It can also be defined as the module of python that helps the developers to develop the cross-platform GUI that follows the Qt standard. It is one of the most important topics in python which is leveraged when there is an urge to develop the application's UI which could work fine in any of the platforms. The sole purpose of PySide is to endorse the application UI development where the application is intended to serve a large set of audience and its platform that could be used by the user is unknown to the organization. It doesn't take too much effort to get implemented. Its primary objective is to help one in accessing the Qt libraries that work as the base while developing the cross-platform applications. Overview Section 1: Pyside2 Lecture 1 Introduction to PySide2 Course Lecture 2 Difference Between PySide and PySide2 Lecture 3 Installation of PySide2 in PyCharm Lecture 4 Creating Simple Frame in PySide2 Lecture 5 Creating Label in PySide2 Lecture 6 Single Line Text Box in PySide2 Lecture 7 Button in PySide2 Lecture 8 Radio Buttons in PySide2 Lecture 9 Check Box in PySide2 Lecture 10 Combo Box in PySide2 Lecture 11 Text Edit in PySide2 Lecture 12 Table in PySide2 Lecture 13 Slider in PySide2 Lecture 14 QDial in PySide2 Part 1 Lecture 15 QDial in PySide2 Part 2 Lecture 16 QDial in PySide2 Part 3 Lecture 17 ProgressBar in PySide2 Lecture 18 MenuBar in PySide2 Lecture 19 Tabs in PySide2 Lecture 20 TreeView in PySide2 Lecture 21 TreeView in PySide2 Continue Lecture 22 Message Box in PySide2 Lecture 23 Adding layouts in Frame Section 2: PySide2 Project - Data Fetching Application Lecture 24 Introduction to Project Lecture 25 Installation of PySide2 Lecture 26 Creating a CSV File Lecture 27 Creating GUI Lecture 28 Counting No of Rows From CSV File Lecture 29 Reading CSV File Lecture 30 Output of the Project Section 3: Creating a Employee Management Application using PySide2 Lecture 31 Introduction to Project Lecture 32 Installation PySide2 Lecture 33 Creating Main Page Lecture 34 Creating Add Employee Page Lecture 35 Creating Display Employee Page Lecture 36 Writing a CSV File to Add Employee Lecture 37 Reading a CSV File to Display Employees Lecture 38 Output of the Project The target audience for this course can be anyone willing to master PySide and learn all the concepts linked with it. The developers who are working on python or any other programming language and want to learn this can be the best target audience for this course. They will be learning about PySide from scratch which will help them to learn how to implement it. The students who are willing to dive into advanced level aspects of python can also be the best target audience for this course. They will be learning how to implement PySide and the Qt libraries while developing the application. The educators who are already training folks in python and want to cover the advanced aspects can also be the best target audience for this PySide training. They will be able to train their students about PySide once they are done with this training. 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  20. Instructors: Bluelime Learning Solutions | 4 sections • 21 lectures • 1h 55m total length Video: MP4 1280x720 44 KHz | English + Sub | Updated 7/2022 | Size: 555 MB Learn how to connect to a MySQL database with Python What you'll learn Install Python Create and activate virtual environment Build a Python App Install Python to MySQL Database Connector Connect Python App to MySQL Database Interact with database from Python App Requirements Python and MySQL Installation required. ( This is covered in the course) Description Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems. This versatility, along with its beginner-friendliness, has made it one of the most-used programming languages today. Python, one of the most popular programming languages in the world, has created everything from Netflix's recommendation algorithm to the software that controls self-driving cars. Python is a general-purpose language, which means it's designed to be used in a range of applications, including data science, software and web development, automation, and generally getting stuff done. Python has become one of the most popular programming languages in the world in recent years. It's used in everything from machine learning to building websites and software testing. It can be used by developers and non-developers alike. MySQL, the most popular Open Source SQL database management system, is developed, distributed, and supported by Oracle Corporation.; A database is a structured collection of data. It may be anything from a simple shopping list to a picture gallery or the vast amounts of information in a corporate network. To add, access, and process data stored in a computer database, you need a database management system such as MySQL Server. In this course we will build a Python app and connect it to MySQL Database .; Once Python has been connected to the database they can begin to interact with each other. Who this course is for:Anyone with basic knowledge of Python and MySQL Homepage https://www.udemy.com/course/python-database-connection-connect-python-app-to-mysql/ Download from UploadCloud https://www.uploadcloud.pro/c8ndfyij6pho/hzugl.P.D.C.C.P.A.t.M.rar.html Download ( Rapidgator ) https://rapidgator.net/file/5c915c3078b296e90fd7b83cf58f7017/hzugl.P.D.C.C.P.A.t.M.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/645f8afaEe2A3758/hzugl.P.D.C.C.P.A.t.M.rar [b]nitroflare.com[/b]: https://nitroflare.com/view/09429454EA4DE1C/hzugl.P.D.C.C.P.A.t.M.rar Links are Interchangeable - No Password - Single Extraction
  21. Published 07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 14 lectures (2h 19m) | Size: 876.8 MB You will Do Deferent real-world python projects as a beginner python programmer. What you'll learn You will Do Deferent real world python projects All the python projects are very simple and easy to follow All of these projects are real-world projects you can use for your CV If you follow and do those real world projects you can build your confidence to do other advanced python projects for your self. Requirements You Need Only internet Connection and Computer To Start and Do Those Python Projects. Description Description If you are new to python programming this course helps you to learn python fast. In this course, I will put different real-world python projects which help you to learn python as fast as possible by doing real fully functional projects. Generally, to list Some of the projects you will do is, · Generating Barcodes By Using Python Programing Language · Internet Download Speed Test python project · Converting Text To Speech By Using Python Programing Language · Converting Video To GIF By Using Python Programing Language · Extract Texts from PDF By Using Python · Link (URL) Shortener python project · Computer CPU Usage Calculator Python Project · Creating Audiobook by using python · Converting Video to Audio Python Project · Automate google search by using python · Creating a digital clock By Using Python Programing Language · Write a Program to sum two numbers · Create Your Own Python Animation · Python Program to Make a Simple Calculator using functions · Battery Notifier for Laptop program python project · Copy contents of one file to another file Python project · Correct Spellings Python project · Download YouTube videos python project · Extract Text from the Images python project · fake name and address generator python project · Generate QR Code Python project · Generate Text Python project · Get Phone Number Details Python project · Password Generator Python Project · Sending Emails With python project · Taking Python project · Text Language Translation python project · Voice Recorder Python Project And many more real-world python projects Who this course is for This Course Is For Some one Who Wants to learn Python Programing Language With Projects. you can learn python By doing projects. Homepage https://www.udemy.com/course/do-different-real-world-python-projects-as-a-beginner/ Download ( Rapidgator ) https://rapidgator.net/file/7d45563f6c0a232016bba5bff1b8e32f/ktgsv.Do.Different.RealWorld.Python.Projects.as.a.Beginner.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/fE10310Be7d24E12/ktgsv.Do.Different.RealWorld.Python.Projects.as.a.Beginner.rar [b]nitroflare.com[/b]: https://nitroflare.com/view/0282107E81579DC/ktgsv.Do.Different.RealWorld.Python.Projects.as.a.Beginner.rar Links are Interchangeable - No Password - Single Extraction
  22. Published 07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 14 lectures (2h 19m) | Size: 876.8 MB You will Do Deferent real-world python projects as a beginner python programmer. What you'll learn You will Do Deferent real world python projects All the python projects are very simple and easy to follow All of these projects are real-world projects you can use for your CV If you follow and do those real world projects you can build your confidence to do other advanced python projects for your self. Requirements You Need Only internet Connection and Computer To Start and Do Those Python Projects. Description Description If you are new to python programming this course helps you to learn python fast. In this course, I will put different real-world python projects which help you to learn python as fast as possible by doing real fully functional projects. Generally, to list Some of the projects you will do is, · Generating Barcodes By Using Python Programing Language · Internet Download Speed Test python project · Converting Text To Speech By Using Python Programing Language · Converting Video To GIF By Using Python Programing Language · Extract Texts from PDF By Using Python · Link (URL) Shortener python project · Computer CPU Usage Calculator Python Project · Creating Audiobook by using python · Converting Video to Audio Python Project · Automate google search by using python · Creating a digital clock By Using Python Programing Language · Write a Program to sum two numbers · Create Your Own Python Animation · Python Program to Make a Simple Calculator using functions · Battery Notifier for Laptop program python project · Copy contents of one file to another file Python project · Correct Spellings Python project · Download YouTube videos python project · Extract Text from the Images python project · fake name and address generator python project · Generate QR Code Python project · Generate Text Python project · Get Phone Number Details Python project · Password Generator Python Project · Sending Emails With python project · Taking Python project · Text Language Translation python project · Voice Recorder Python Project And many more real-world python projects Who this course is for This Course Is For Some one Who Wants to learn Python Programing Language With Projects. you can learn python By doing projects. Homepage https://www.udemy.com/course/do-different-real-world-python-projects-as-a-beginner/ Download ( Rapidgator ) https://rapidgator.net/file/7d45563f6c0a232016bba5bff1b8e32f/ktgsv.Do.Different.RealWorld.Python.Projects.as.a.Beginner.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/fE10310Be7d24E12/ktgsv.Do.Different.RealWorld.Python.Projects.as.a.Beginner.rar [b]nitroflare.com[/b]: https://nitroflare.com/view/0282107E81579DC/ktgsv.Do.Different.RealWorld.Python.Projects.as.a.Beginner.rar Links are Interchangeable - No Password - Single Extraction
  23. Instructors: Bluelime Learning Solutions | 1 section • 12 lectures • 53m total length Video: MP4 1280x720 44 KHz | English + Sub | Updated 7/2022 | Size: 320 MB Improve your Python development skills What you'll learn Build a currency converter app Install Python Install Text Editor Design the app Create a Python Module Build the app interface with tkinter Create functions for the app Requirements Python installation required. ( The course shows you how to install Python ) Description Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems. This versatility, along with its beginner-friendliness, has made it one of the most-used programming languages today. Python has become one of the most popular programming languages in the world in recent years. It's used in everything from machine learning to building websites and software testing. It can be used by developers and non-developers alike. Python, one of the most popular programming languages in the world, has created everything from Netflix's recommendation algorithm to the software that controls self-driving cars. Python is a general-purpose language, which means it's designed to be used in a range of applications, including data science, software and web development, automation, and generally getting stuff done A currency converter is an app or tool that allows you to quickly convert from one currency to another. We will build our own currency converter app from scratch step by step using Python.; We will build the interface for the app and also create the Python functions that will make it work and perform the currency conversions. Who this course is for:Anyone with a basic knowledge of Python Homepage https://www.udemy.com/course/build-a-currency-converter-app-with-python/ Download from UploadCloud https://www.uploadcloud.pro/pcz21rn7vav6/bsohb.B.a.C.C.A.w.P.rar.html Download ( Rapidgator ) https://rapidgator.net/file/8fc86d700b6d8b4fb13c3a491694ec6a/bsohb.B.a.C.C.A.w.P.rar.html [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/9ED66Bc92702376f/bsohb.B.a.C.C.A.w.P.rar [b]nitroflare.com[/b]: https://nitroflare.com/view/566EDB616E75B99/bsohb.B.a.C.C.A.w.P.rar Links are Interchangeable - No Password - Single Extraction
  24. Published 07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 33 lectures (7h 1m) | Size: 2.92 GB BERT, GPT, Deep Learning, Machine Learning, & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch, & Keras What you'll learn Chunking Bag of Words Hugging Face transformer POS tagging TF-IDF GPT-2 Token Classification BERT Stemming Lemmatization NER Preprocessing data Attention Fine-tuning Requirements Expert in Pytorch Expert in Recurrent Neural Network Expert in Python programming language Description Interested in the field of Natural Language Processing (NLP)? Then this course is for you! This course has been designed by a software engineer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way. I will walk you step-by-step into the transformer which is a very powerful tool in Natural Language Processing. With every tutorial, you will develop new skills and improve your understanding of transformers in Natural Language Processing. This course is fun and exciting, but at the same time, we dive deep into transformer. Throughout the brand new version of the course, we cover tons of tools and technologies including Deep Learning. Google Colab Keras. Matplotlib. Splitting Data into Training Set and Test Set. Training Neural Network. Model building. Analyzing Results. Model compilation. Make a Prediction. Testing Accuracy. Confusion Matrix. Numpy. Pandas. Tensorflow. Chunking Bag of Words Hugging Face transformer POS tagging TF-IDF GPT-2 Token Classification BERT Stemming Lemmatization NER Preprocessing data. Attention Fine-tuning Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are several projects for you to practice and build up your knowledge. These projects are listed below Gender Identification. Sentiment Analyzer. Topic Modelling IMDB Project. QA project. Text generation project. Who this course is for Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence Anyone passionate about Artificial Intelligence Anyone interested in Natural Language Processing Data Scientists who want to take their AI Skills to the next level Homepage https://www.udemy.com/course/la-hoang-quy-introduction-to-transformer-for-nlp-with-python/ Download ( Rapidgator ) https://rapidgator.net/file/33103c101f049c289f495a13cfafb616/yheam.Introduction.to.Transformer.for.NLP.with.Python.part2.rar.html https://rapidgator.net/file/93ce8f72f39c4039051e5c2af9facd96/yheam.Introduction.to.Transformer.for.NLP.with.Python.part1.rar.html https://rapidgator.net/file/f2672859519ab5a55638e0fbed523db5/yheam.Introduction.to.Transformer.for.NLP.with.Python.part3.rar.html Download ( NitroFlare ) https://nitro.download/view/1FA542F47C47DDA/yheam.Introduction.to.Transformer.for.NLP.with.Python.part2.rar https://nitro.download/view/3D95657EB056A43/yheam.Introduction.to.Transformer.for.NLP.with.Python.part3.rar https://nitro.download/view/7E1392F973303D4/yheam.Introduction.to.Transformer.for.NLP.with.Python.part1.rar [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/3B1A64bc7133b140/yheam.Introduction.to.Transformer.for.NLP.with.Python.part2.rar https://uploadgig.com/file/download/562210d5D01c0bc5/yheam.Introduction.to.Transformer.for.NLP.with.Python.part3.rar https://uploadgig.com/file/download/a302bfd1222209De/yheam.Introduction.to.Transformer.for.NLP.with.Python.part1.rar Links are Interchangeable - No Password - Single Extraction
  25. Published 07/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 22 lectures (4h 13m) | Size: 1 GB A course designed for beginners to learn computer vision and data science projects! What you'll learn Python advanced Project working knowledge Hands-on experience Programming knowledge Requirements Basic Python knowledge Description In this course, we will be covering the basic to intermediate level of python projects. We will cover 15-20 projects with quizzes for your practice. I will also be giving you instructions regarding the software installation. Feel free to drop your doubts and give me a rating at the end of the course. So, from this course, you will get hands-on experience to work on python projects. The coding level will be from beginner to intermediate-level learners. At the end of this course, you will be able to do basic python projects with Pycharm. The learning objectives will be mostly on computer vision and data science coding. You will learn some of the modules and packages with detailed explanations. This course will help you in your college projects. In future, if you work as a Python developer, this course would be a platform for that. You need not worry about your doubts, I'm here to answer. Drop your doubts in the comments section. You are not alone, I'll help you to solve your challenges! At last, I have designed this course for students who struggle to develop python projects. This would help them in future for their jobs in coding. So, why are you waiting? Dive into the course, let's learn together. Who this course is for Python project developers Beginners who want to learn Computer Vision and Data Science basics Homepage https://www.udemy.com/course/aamirp-python_projects/ Download ( Rapidgator ) https://rapidgator.net/file/2d0a03281c8d4e9fab9c5ee986ce9bd1/vbmlh.Python.Projects.2022.by.Aamir.P.part2.rar.html https://rapidgator.net/file/ebb5a18d27b75ffe7437f442266d9418/vbmlh.Python.Projects.2022.by.Aamir.P.part1.rar.html Download ( NitroFlare ) https://nitro.download/view/86E151AE49D46DA/vbmlh.Python.Projects.2022.by.Aamir.P.part1.rar https://nitro.download/view/88C300405E1A92E/vbmlh.Python.Projects.2022.by.Aamir.P.part2.rar [b]Download (Uploadgig)[/b] https://uploadgig.com/file/download/F86136cdf6d70dEA/vbmlh.Python.Projects.2022.by.Aamir.P.part1.rar https://uploadgig.com/file/download/b1C4039cb92e31f8/vbmlh.Python.Projects.2022.by.Aamir.P.part2.rar Links are Interchangeable - No Password - Single Extraction
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