Machine Learning 2021: Practical Regression Analysis in R
MP4 | h264, 1280x720 | Lang: English | Audio: aac, 44100 Hz | 4h 28m | 1.45 GB
What you'll learn

Your comprehensive guide to Regression Analysis & supervised machine learning using R-programming language
Graphically representing data in R before and after analysis
It covers the theory and applications of supervised machine learning with the focus on regression analysis using the R-programming language in R-Studio
Implement Ordinary Least Square (or simple linear) regression, Random FOrest Regression, Decision Trees, Logistic regression and others using R
Perform model's variable selection and assess regression model's accuracy
Build machine learning based regression models and test their performance in R
Compare different different machine learning models for regression tasks in R
Learn how to select the best statistical & machine learning model for your task
Learn when and how machine learning models should be applied
Carry out coding exercises & your independent project assignment

Requirements
Availabiliy computer and internet & strong interest in the topic
Description
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:

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