Regression Analysis in R for Data Science: from Zero to Hero
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Regression Analysis for Machine Learning & Data Science in R
My course will be your hands-on guide to the theory and applications of supervised machine learning with the focus on regression analysis using the R-programming language.
Unlike other courses, it offers NOT ONLY the guided demonstrations of the R-scripts but also covers theoretical background that will allow you to FULLY UNDERSTAND & APPLY REGRESSION ANALYSIS (Linear Regression, Random Forest, KNN, etc) in R (many R packages incl. caret package will be covered) for supervised machine learning and prediction tasks.
This course also covers all the main aspects of practical and highly applied data science related to Machine Learning (i.e. regression analysis). Thus, if you take this course, you will save lots of time & money on other expensive materials in the R based Data Science and Machine Learning domain.
THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF MACHINE LEARNING: BOTH THEORY & PRACTISE
- Fully understand the basics of Regression Analysis (parametric & non-parametric methods) & supervised Machine Learning from theory to practice
- Harness applications of parametric and non-parametric regressions in R
- Learn how to apply correctly regression models and test them in R
- Learn how to select the best statistical & machine learning model for your task
- Carry out coding exercises & your independent project assignment
- Learn the basics of R-programming
- Get a copy of all scripts used in the course
- and MORE
NO PRIOR R OR STATISTICS/MACHINE LEARNING / R KNOWLEDGE REQUIRED:
You’ll start by absorbing the most valuable Regression Analysis & R-programming basics, and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Thus, after completing my Regression Analysis for Machine Learning in R course, you’ll easily use different data streams and data science packages to work with real data in R.
In case it is your first encounter with R, don’t worry, my course a full introduction to the R & R-programming in this course.
This course is different from other training resources. Each lecture seeks to enhance your Regression modeling and Machine Learning skills in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions. You’ll be able to start analyzing different streams of data for your projects and gain appreciation from your future employers with your improved machine learning skills and knowledge of cutting edge data science methods.
The course is ideal for professionals who need to use cluster analysis, unsupervised machine learning, and R in their field.
One important part of the course is the practical exercises. You will be given some precise instructions and datasets to run Machine Learning algorithms using the R tools.
JOIN MY COURSE NOW!
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1IntroductionVideo lesson
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2Introduction to Regression AnalysisVideo lesson
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3Introduction to Regression AnalysisQuiz
To test your understanding on Regression Analysis
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4What is Machine Leraning and it's main types?Video lesson
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5Overview of Machine Leraning in RVideo lesson
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6Machine Learning TypesQuiz
Machine Learning Types
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7Introduction to Section 2Video lesson
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8What is R and RStudio?Video lesson
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9How to install R and RStudio in 2020Video lesson
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10Lab: Install R and RStudio in 2020Video lesson
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11Introduction to RStudio InterfaceVideo lesson
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12Lab: Get started with R in RStudioVideo lesson
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13What is the latest version of RStudio and R?Quiz
Test your knowledge of R and RStudio
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14Introduction to Section 3Video lesson
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15Lab: Installing Packages and Package Management in RVideo lesson
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16Variables in R and assigning Variables in RVideo lesson
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17Lab: Variables in R and assigning Variables in RVideo lesson
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18Overview of data types and data structures in RVideo lesson
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19Lab: data types and data structures in RVideo lesson
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20Vectors' operations in RVideo lesson
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21Data types and data structures: FactorsVideo lesson
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22Dataframes: overviewVideo lesson
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23Functions in R - overviewVideo lesson
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24Lab: For Loops in RVideo lesson
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25Read Data into RVideo lesson
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26Overview of Regression AnalysisVideo lesson
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27Overview of Regression AnalysisQuiz
Overview of Regression Analysis
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28Graphical Analysis of Regression ModelsVideo lesson
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29Your first linear regression model in RVideo lesson
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30Lab: Correlation & Linear Regression Analysis in RVideo lesson
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31How to know if the model is best fit for your data - theoryVideo lesson
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32Lab: Linear Regression DiagnosticsVideo lesson
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33Lab how to measure the linear model's fit: AIC and BICVideo lesson
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34Evaluation of Prediction Model Performance in Supervised Learning: RegressionVideo lesson
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35Predict with linear regression model & RMSE as in-sample errorVideo lesson
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36Prediction model evaluation with data split: out-of-sample RMSEVideo lesson
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37Lab: Multiple linear regression - model estimationVideo lesson
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38Lab: Multiple linear regression - predictionVideo lesson
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39Lab: Multiple linear regression with interactionVideo lesson
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40Regression with Categorical Variables: Dummy Coding Essentials in RVideo lesson
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41ANOVA - Categorical variables with more than two levels in linear regressionsVideo lesson
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