kingers Posted April 26 Report Share Posted April 26 Data Science In Python: Unsupervised Learning Published 4/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 5.09 GB[/center] | Duration: 16h 47m Learn Python for Data Science & Machine Learning, and build unsupervised learning models with fun, hands-on projects What you'll learn Master the foundations of unsupervised Machine Learning in Python, including clustering, anomaly detection, dimensionality reduction, and recommenders Prepare data for modeling by applying feature engineering, selection, and scaling Fit, tune, and interpret three types of clustering algorithms: K-Means Clustering, Hierarchical Clustering, and DBSCAN Use unsupervised learning techniques like Isolation Forests and DBSCAN for anomaly detection Apply and interpret two types of dimensionality reduction models: Principal Component Analysis (PCA) and t-SNE Build recommendation engines using content-based and collaborative filtering techniques, including Cosine Similarity and Singular Value Decomposition (SVD) Requirements We strongly recommend taking our Data Prep & EDA course before this one Jupyter Notebooks (free download, we'll walk through the install) Familiarity with base Python and Pandas is recommended, but not required Description This is a hands-on, project-based course designed to help you master the foundations for unsupervised learning in Python.We'll start by reviewing the data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You'll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.From there we'll fit, tune, and interpret 3 popular clustering models using scikit-learn. We'll start with K-Means Clustering, learn to interpret the output's cluster centers, and use inertia plots to select the right number of clusters. Next, we'll cover Hierarchical Clustering, where we'll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we'll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.We'll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You'll learn to tune and interpret the results of each model and visualize the anomalies using pair plots.Next, we'll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We'll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization.Last but not least, we'll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.Throughout the course you'll play the role of an Associate Data Scientist for the HR Analytics team at a software company trying to increase employee retention. Using the skills you learn throughout the course, you'll use Python to segment the employees, visualize the clusters, and recommend next steps to increase retention.COURSE OUTLINE:Intro to Data ScienceIntroduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflowUnsupervised Learning 101Review the basics of unsupervised learning, including key concepts, types of techniques and applications, and its place in the data science workflowPre-Modeling Data PrepRecap the data prep steps required to apply unsupervised learning models, including restructuring data, engineering & scaling features, and moreClusteringApply three different clustering techniques in Python and learn to interpret their results using metrics, visualizations, and domain expertiseAnomaly DetectionUnderstand where anomaly detection fits in the data science workflow, and apply techniques like Isolation Forests and DBSCAN in PythonDimensionality ReductionUse techniques like Principal Component Analysis (PCA) and t-SNE in Python to reduce the number of features in a data set without losing informationRecommendersRecognize the variety of approaches for creating recommenders, then apply unsupervised learning techniques in Python, including Cosine Similarity and Singular Vector Decomposition (SVD)__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:16.5 hours of high-quality video22 homework assignments7 quizzes3 projectsData Science in Python: Unsupervised Learning ebook (350+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you're an aspiring or seasoned data scientist looking for a practical overview of unsupervised learning techniques in Python with a focus on interpretation, this is the course for you.Happy learning!-Alice Zhao (Python Expert & Data Science Instructor, Maven Analytics) Overview Section 1: Getting Started Lecture 1 Course Introduction Lecture 2 About This Series Lecture 3 Course Structure & Outline Lecture 4 READ ME: Important Notes for New Students Lecture 5 DOWNLOAD: Course Resources Lecture 6 Introducing the Course Project Lecture 7 Setting Expectations Lecture 8 Jupyter Installation & Launch Section 2: Intro to Data Science Lecture 9 Section Introduction Lecture 10 What is Data Science? Lecture 11 Data Science Skill Set Lecture 12 What is Machine Learning? Lecture 13 Common Machine Learning Algorithms Lecture 14 Data Science Workflow Lecture 15 Step 1: Scoping a Project Lecture 16 Step 2: Gathering Data Lecture 17 Step 3: Cleaning Data Lecture 18 Step 4: Exploring Data Lecture 19 Step 5: Modeling Data Lecture 20 Step 6: Sharing Insights Lecture 21 Unsupervised Learning Lecture 22 Key Takeaways Section 3: Unsupervised Learning 101 Lecture 23 Section Introduction Lecture 24 Unsupervised Learning 101 Lecture 25 Unsupervised Learning Techniques Lecture 26 Unsupervised Learning Applications Lecture 27 Structure of This Course Lecture 28 Unsupervised Learning Workflow Lecture 29 Key Takeaways Section 4: Pre-Modeling Data Prep Lecture 30 Section Introduction Lecture 31 Data Prep for Unsupervised Learning Lecture 32 Setting the Correct Row Granularity Lecture 33 DEMO: Group By Lecture 34 DEMO: Pivot Lecture 35 ASSIGNMENT: Setting the Correct Row Granularity Lecture 36 SOLUTION: Setting the Correct Row Granularity Lecture 37 Preparing Columns for Modeling Lecture 38 Identifying Missing Data Lecture 39 Handling Missing Data Lecture 40 Converting to Numeric Lecture 41 Converting to DateTime Lecture 42 Extracting DateTime Lecture 43 Calculating Based on a Condition Lecture 44 Dummy Variables Lecture 45 ASSIGNMENT: Preparing Columns for Modeling Lecture 46 SOLUTION: Preparing Columns for Modeling Lecture 47 Feature Engineering Lecture 48 Feature Engineering During Data Prep Lecture 49 Applying Calculations Lecture 50 Binning Values Lecture 51 Identifying Proxy Variables Lecture 52 Feature Engineering Tips Lecture 53 ASSIGNMENT: Feature Engineering Lecture 54 SOLUTION: Feature Engineering Lecture 55 Excluding Identifiers From Modeling Lecture 56 Feature Selection Lecture 57 ASSIGNMENT: Feature Selection Lecture 58 SOLUTION: Feature Selection Lecture 59 Feature Scaling Lecture 60 Normalization Lecture 61 Standardization Lecture 62 ASSIGNMENT: Feature Scaling Lecture 63 SOLUTION: Feature Scaling Lecture 64 Key Takeaways Section 5: Clustering Lecture 65 Section Introduction Lecture 66 Clustering Basics Lecture 67 K-Means Clustering Lecture 68 K-Means Clustering in Python Lecture 69 DEMO: K-Means Clustering in Python Lecture 70 Visualizing K-Means Clustering Lecture 71 Interpreting K-Means Clustering Lecture 72 Visualizing Cluster Centers Lecture 73 ASSIGNMENT: K-Means Clustering Lecture 74 SOLUTION: K-Means Clustering Lecture 75 Inertia Lecture 76 Plotting Inertia in Python Lecture 77 DEMO: Plotting Inertia in Python Lecture 78 ASSIGNMENT: Inertia Plot Lecture 79 SOLUTION: Inertia Plot Lecture 80 Tuning a K-Means Model Lecture 81 DEMO: Tuning a K-Means Model Lecture 82 ASSIGNMENT: Tuning a K-Means Model Lecture 83 SOLUTION: Tuning a K-Means Model Lecture 84 Selecting the Best Model Lecture 85 DEMO: Selecting the Best Model Lecture 86 ASSIGNMENT: Selecting the Best K-Means Model Lecture 87 SOLUTION: Selecting the Best K-Means Model Lecture 88 Hierarchical Clustering Lecture 89 Dendrograms in Python Lecture 90 Agglomerative Clustering in Python Lecture 91 DEMO: Agglomerative Clustering in Python Lecture 92 Cluster Maps in Python Lecture 93 DEMO: Cluster Maps in Python Lecture 94 ASSIGNMENT: Hierarchical Clustering Lecture 95 SOLUTION: Hierarchical Clustering Lecture 96 DBSCAN Lecture 97 DBSCAN in Python Lecture 98 Silhouette Score Lecture 99 Silhouette Score in Python Lecture 100 DEMO: DBSCAN and Silhouette Score in Python Lecture 101 ASSIGNMENT: DBSCAN Lecture 102 SOLUTION: DBSCAN Lecture 103 Comparing Clustering Algorithms Lecture 104 Clustering Next Steps Lecture 105 DEMO: Compare Clustering Models Lecture 106 DEMO: Label Unseen Data Lecture 107 Key Takeaways Section 6: PROJECT: Clustering Clients Lecture 108 Project Overview Lecture 109 SOLUTION: Data Prep Lecture 110 SOLUTION: K-Means Clustering Lecture 111 SOLUTION: Hierarchical Clustering Lecture 112 SOLUTION: DBSCAN Lecture 113 SOLUTION: Compare, Recommend and Predict Section 7: Anomaly Detection Lecture 114 Section Introduction Lecture 115 Anomaly Detection Basics Lecture 116 Anomaly Detection Approaches Lecture 117 Anomaly Detection Workflow Lecture 118 Isolation Forests Lecture 119 Isolation Forests in Python Lecture 120 Visualizing Anomalies Lecture 121 Tuning and Interpreting Isolation Forests Lecture 122 ASSIGNMENT: Isolation Forests Lecture 123 SOLUTION: Isolation Forests Lecture 124 DBSCAN for Anomaly Detection Lecture 125 DBSCAN for Anomaly Detection in Python Lecture 126 Visualizing DBSCAN Anomalies Lecture 127 ASSIGNMENT: DBSCAN for Anomaly Detection Lecture 128 SOLUTION: DBSCAN for Anomaly Detection Lecture 129 Comparing Anomaly Detection Algorithms Lecture 130 RECAP: Clustering and Anomaly Detection Lecture 131 Key Takeaways Section 8: Dimensionality Reduction Lecture 132 Section Introduction Lecture 133 Dimensionality Reduction Basics Lecture 134 Why Reduce Dimensions? Lecture 135 Dimensionality Reduction Workflow Lecture 136 Principal Component Analysis Lecture 137 Principal Component Analysis in Python Lecture 138 Explained Variance Ratio Lecture 139 DEMO: PCA and Explained Variance Ratio in Python Lecture 140 ASSIGNMENT: Principal Component Analysis Lecture 141 SOLUTION: Principal Component Analysis Lecture 142 Interpreting PCA Lecture 143 DEMO: Interpreting PCA Lecture 144 ASSIGNMENT: Interpreting PCA Lecture 145 SOLUTION: Interpreting PCA Lecture 146 Feature Selection vs Feature Extraction Lecture 147 PCA Next Steps Lecture 148 T-SNE Lecture 149 T-SNE in Python Lecture 150 ASSIGNMENT: T-SNE Lecture 151 SOLUTION: T-SNE Lecture 152 PCA vs t-SNE Lecture 153 DEMO: Dimensionality Reduction and Clustering Lecture 154 ASSIGNMENT: T-SNE & K-Means Clustering Lecture 155 SOLUTION: T-SNE & K-Means Clustering Lecture 156 Key Takeaways Section 9: Recommenders Lecture 157 Section Introduction Lecture 158 Recommenders Basics Lecture 159 Content-Based Filtering Lecture 160 Cosine Similarity Lecture 161 Cosine Similarity in Python Lecture 162 Making a Content Based Filtering Recommendation Lecture 163 ASSIGNMENT: Content-Based Filtering Lecture 164 SOLUTION: Content-Based Filtering Lecture 165 Collaborative Filtering Lecture 166 User-Item Matrix Lecture 167 ASSIGNMENT: User-Item Matrix Lecture 168 SOLUTION: User-Item Matrix Lecture 169 Singular Value Decomposition Lecture 170 Singular Value Decomposition in Python Lecture 171 ASSIGNMENT: Singular Value Decomposition Lecture 172 SOLUTION: Singular Value Decomposition Lecture 173 Choosing the Number of Components Lecture 174 DEMO: Choosing the Number of Components Lecture 175 ASSIGNMENT: Choosing the Number of Components Lecture 176 SOLUTION: Choosing the Number of Components Lecture 177 Making a Collaborative Filtering Recommendation Lecture 178 DEMO: Making a Collaborative Filtering Recommendation Lecture 179 ASSIGNMENT: Collaborative Filtering Lecture 180 SOLUTION: Collaborative Filtering Lecture 181 Recommender Next Steps Lecture 182 DEMO: Hybrid Approach Lecture 183 Key Takeaways Section 10: PROJECT: Recommending Restaurants Lecture 184 Project Overview Lecture 185 SOLUTION: Data Prep Lecture 186 SOLUTION: TruncatedSVD Lecture 187 SOLUTION: Cosine Similarity Lecture 188 SOLUTION: Recommendations Section 11: Unsupervised Learning Review Lecture 189 Section Introduction Lecture 190 Unsupervised Learning Flow Chart Lecture 191 Unsupervised Learning Techniques & Applications Lecture 192 Unsupervised Learning in the Data Science Workflow Lecture 193 Key Takeaways Section 12: Final Project Lecture 194 Final Project Overview Lecture 195 SOLUTION: Data Prep & EDA Lecture 196 SOLUTION: Clustering Lecture 197 SOLUTION: PCA Lecture 198 SOLUTION: Clustering (Round 2) Lecture 199 SOLUTION: PCA (Round 2) Lecture 200 SOLUTION: EDA on Clusters Lecture 201 SOLUTION: Recommendations Section 13: Next Steps Lecture 202 BONUS LESSON Data scientists who want to learn how to build and interpret unsupervised learning models in Python,Analysts or BI experts looking to learn about unsupervised learning or transition into a data science role,Anyone interested in learning one of the most popular open source programming languages in the worldhttps://voltupload.com/pjxe0dewm2a0/Data_Science_in_Python_Unsupervised_Learning.z01https://voltupload.com/p3z3enkxo41k/Data_Science_in_Python_Unsupervised_Learning.z02https://voltupload.com/v6ipubz3j3lb/Data_Science_in_Python_Unsupervised_Learning.z03https://voltupload.com/wikoprjeftit/Data_Science_in_Python_Unsupervised_Learning.z04https://voltupload.com/j1ghsbjq70k5/Data_Science_in_Python_Unsupervised_Learning.z05https://voltupload.com/kkpbjnuaaplg/Data_Science_in_Python_Unsupervised_Learning.z06https://voltupload.com/b43wzmuh1rcx/Data_Science_in_Python_Unsupervised_Learning.ziphttps://rapidgator.net/file/06194d16abe956a259e6a65b3c8d567e/Data_Science_in_Python_Unsupervised_Learning.z01https://rapidgator.net/file/fec6113f65c83ac7015c019140626e45/Data_Science_in_Python_Unsupervised_Learning.z02https://rapidgator.net/file/c5c19f444e47669aa5099b4afcc1ea45/Data_Science_in_Python_Unsupervised_Learning.z03https://rapidgator.net/file/d743ff6f10e1b5369fe6aa1ac4489608/Data_Science_in_Python_Unsupervised_Learning.z04https://rapidgator.net/file/b33f62f0272c6a64fa151804f3c814e8/Data_Science_in_Python_Unsupervised_Learning.z05https://rapidgator.net/file/08ceb4fbc9bf79c33117846d89499ecd/Data_Science_in_Python_Unsupervised_Learning.z06https://rapidgator.net/file/5b2d7682437e0f18576d61db0642ccef/Data_Science_in_Python_Unsupervised_Learning.zipFree search engine download: Data Science in Python Unsupervised Learning Link to comment Share on other sites More sharing options...
Recommended Posts
Create an account or sign in to comment
You need to be a member in order to leave a comment
Create an account
Sign up for a new account in our community. It's easy!
Register a new accountSign in
Already have an account? Sign in here.
Sign In Now