riversongs Posted March 23 Report Share Posted March 23 Free Download Regression Analysis for Statistics & Machine Learning in RLast updated 11/2023Duration: 7h 43m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 4.04 GBGenre: eLearning | Language: EnglishLearn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in RWhat you'll learnImplement and infer Ordinary Least Square (OLS) regression using RApply statistical and machine learning based regression models to deals with problems such as multicollinearityCarry out variable selection and assess model accuracy using techniques like cross-validationImplement and infer Generalized Linear Models (GLMS), including using logistic regression as a binary classifierBuild machine learning based regression models and test their robustness in RLearn when and how machine learning models should be appliedCompare different different machine learning algorithms for regression modellingRequirementsShould have prior experience of working with R and RStudioShould have basic knowledge of statisticsShould have prior experience of using simple linear regression modellingShould have interest in building on the previous concepts to learn which regression models are applicable under different circumstancesShould have an interest in learning the machine learning based regression models in RDescriptionWith so many R Statistics & Machine Learning courses around, why enrol for this?Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.My name isMINERVA SINGHand I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished aPhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. This course is based on my years of regression modelling experience and implementing different regression models on real life data. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course will change this. You will go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models.Become a Regression Analysis Expert and Harness the Power of R for Your AnalysisGet started with R and RStudio. Install these on your system, learn to load packages and read in different types of data in RCarry out data cleaning and data visualization using RImplement ordinary least square (OLS) regression in R and learn how to interpret the results.Learn how to deal with multicollinearity both through variable selection and regularization techniques such as ridge regressionCarry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.Evaluate regression model accuracyImplement generalized linear models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data.Work with tree-based machine learning modelsImplement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.Carry out model selectionBecome a Regression Analysis Pro and Apply Your Knowledge on Real-Life DataThis course is your one shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in a renowned international journal likePLOS One. Specifically, the course will(a) Take the students with a basic level of statistical knowledge to perform some of the most common advanced regression analysis based techniques(b) Equip students to use R for performing the different statistical and machine learning data analysis and visualization tasks(c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation(d) Students will get a strong background in some of the most important statistical and machine learning concepts for regression analysis.(e) Students will be able to decide which regression analysis techniques are best suited to answer their research questions and applicable to their data and interpret the resultsIt is apractical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you may apply to your own projects.TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.Who this course is forPeople who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience)People with basic knowledge of R based statistical modellingPeople with knowledge of linear regression modellingPeople wanting to extend their knowledge of regression modelling for solving real world problems.People wanting to learn how to apply machine learning based regression models using RUndergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysisAcademic researchers seeking to learn new techniques for data analysisBusiness data analysts who wish to use regression modelling for predictive analysisHomepagehttps://www.udemy.com/course/regression-analysis-for-statistics-machine-learning-in-rDownload ( Rapidgator )https://rg.to/file/ff626997e7f1afebfcd4e98baa3b6ddc/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part1.rar.htmlhttps://rg.to/file/496a5e1b197ad1b81bb4f534535c06a5/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part2.rar.htmlhttps://rg.to/file/d486e650fed28bb28470735bc28ec3da/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part3.rar.htmlhttps://rg.to/file/c75ff8635a458905381fca9e24a681b3/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part4.rar.htmlhttps://rg.to/file/b21dc2b622954aee4c82ebfafb311764/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part5.rar.htmlUploadgighttps://uploadgig.com/file/download/8e82b5c4391C5cDe/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part1.rarhttps://uploadgig.com/file/download/3b73e001ac12C91F/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part2.rarhttps://uploadgig.com/file/download/82a38Ce9a3d494FC/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part3.rarhttps://uploadgig.com/file/download/ef1ff0daD9760043/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part4.rarhttps://uploadgig.com/file/download/Fb2e5344f8697e14/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part5.rarDownload ( NitroFlare )https://nitroflare.com/view/90EFCE675D4CF9E/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part1.rarhttps://nitroflare.com/view/1AFBF98F804A92D/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part2.rarhttps://nitroflare.com/view/DAC21A02380D798/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part3.rarhttps://nitroflare.com/view/4F267A5195F8DA4/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part4.rarhttps://nitroflare.com/view/DD864E6D94FCC86/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part5.rarFikperhttps://fikper.com/99j8bVmvGs/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part1.rar.htmlhttps://fikper.com/PIbqkDkMLH/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part2.rar.htmlhttps://fikper.com/7NrrtysaA0/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part3.rar.htmlhttps://fikper.com/2SWhQLrlHT/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part4.rar.htmlhttps://fikper.com/ycbjcYtNIY/guqhf.Regression.Analysis.for.Statistics..Machine.Learning.in.R.part5.rar.htmlNo Password - Links are Interchangeable Link to comment Share on other sites More sharing options...
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