5.00 (10 reviews)
☑ 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
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
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!
Introduction to the course, Machine Learning & Regression Analysis
Introduction to Regression Analysis
Introduction to Regression Analysis
What is Machine Leraning and it's main types?
Overview of Machine Leraning in R
Machine Learning Types
Software used in this course R-Studio and Introduction to R
Introduction to Section 2
What is R and RStudio?
How to install R and RStudio in 2020
Lab: Install R and RStudio in 2020
Introduction to RStudio Interface
Lab: Get started with R in RStudio
What is the latest version of RStudio and R?
R Crash Course - get started with R-programming in R-Studio
Introduction to Section 3
Lab: Installing Packages and Package Management in R
Variables in R and assigning Variables in R
Lab: Variables in R and assigning Variables in R
Overview of data types and data structures in R
Lab: data types and data structures in R
Vectors' operations in R
Data types and data structures: Factors
Functions in R - overview
Lab: For Loops in R
Read Data into R
Linear Regression Analysis for Supervised Machine Learning in R
Regression: Short Overview
Your first linear regression model in R
Lab: Correlation & Linear Regression Analysis in R
How to know if the model is best fit for your data - theory
Lab: Linear Regression Diagnostics
Lab how to measure the linear model's fit: AIC and BIC
Linear Regression Model Performance Evaluation in R
Evaluation of Prediction Model Performance in Supervised Learning: Regression
Predict with linear regression model & RMSE as in-sample error
Prediction model evaluation with data split: out-of-sample RMSE
More types of regression models
Lab: Multiple linear regression - model estimation
Lab: Multiple linear regression - prediction
Lab: Multiple linear regression with interaction
Regression with Categorical Variables: Dummy Coding Essentials in R
Non-Parametric and Non-Linear Regression Analysis
Classification and Decision Trees (CART): Theory
Lab: Decision Trees in R
Random Forest: Theory
Lab: Random Forest in R
Parametrise Random Forest model
Regression Models' Comparison for MAchine Learning & Final Project
Lab: Machine Learning Models' Comparison & Best Model Selection
Your Final Project
As a beginner in Machine Learning I found this course useful. It is rich in different aspects of methods. Very useful to understand the basics of classical statistical and ML models, very focused on the practical side.
Good level of complexity. It includes basic concepts and the several steps and methods needed to work on regression analysis.
It has been a fantastic experience going through this course on Regression analysis in R. The concept are clearly and simply explained by the instructor which makes the entire exercise very interesting.
That was an exceptionally informative & clear course on Regression Analysis and Machine Learning in r. Many thanks