5.00 (6 reviews)
☑ Analyse and visualize data using Linear Regression
☑ Learn different types of linear regressions (1-dimensional and multi-dimensional models, logistic regressions, ANOVA, etc)
☑ Learn how to interpret and explain machine learning models
☑ Plot the graph of results of Linear Regression to visually analyze the results
☑ Assumptions of linear regression hypothesis testing
☑ Do feature selection and transformations to fine tune machine learning models
☑ Fully understand the basics of Machine Learning & Linear Regression Models from theory to practice
☑ Learn how to deal with the categorical data in your regression modeling and correlation between variables
☑ Learn the basics of R-programming
Practical Linear Regression in R - Hands-On
This course teaches you about the most common & popular technique used in Data Science & Machine Learning: Linear Regression. You will learn the theory as well as applications of different types of linear regression models. At the end of the course, you will completely understand and know how to apply & implement in R linear models, how to run model's diagnostics, and how to know if the model is the best fit for your data, how to check the model's performance and to make predictions.
Linear regression is the simplest machine learning (and thus deep learning) model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:
THIS COURSE HAS 5 SECTIONS COVERING EVERY ASPECT OF LINEAR REGRESSION: BOTH THEORY TO PRACTICE
Fully understand the basics of Machine Learning & Linear Regression Models from theory to practice
Harness applications of linear regression modeling in R
Learn how to apply correctly linear regression models and test them in R
Complete programming & data science exercises and an independent project in R
Learn how to test the model's fit, how to select the most suitable linear models for your data, and make predictions
Learn different types of linear regressions (1-dimensional and multi-dimensional models, logistic regressions, ANCOVA, etc)
Learn how to deal with the categorical data in your regression modeling and correlation between variables
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 Linear Regression basics, and techniques and slowly moving to more complex assignments.
My course will help you implement the methods using real data obtained from different sources. Thus, after completing my Machine Learning course in R, you’ll easily use different data streams and data science packages to work with real data in R.
This course is different from other training resources. Each lecture seeks to enhance your Data Science & Machine Learning in a demonstrable and easy-to-follow manner and provide you with practically implementable solutions.
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.
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Introduction to Regression Analysis and Linear Regression
Introduction to Regression Analysis
What is Machine Leraning and it's main types?
Machine Learning Types
Software used in this course R-Studio and Introduction to R
How to install R and RStudio in 2020
What is the latest version of RStudio and R?
Linear Regression in R
Getting started with linear regression
Lab: your first linear regression model
Correlation in Regression Analysis in R: Lab
How to know if the model is best fit for your data - An overview
Linear Regression Diagnostics
AIC and BIC
Evaluation of Performance of Regression-based Prediction Model
Lab: Predict with linear regression model & RMSE as in-sample error
Prediction model evaluation with data split: out-of-sample RMSE
More types of linear regression models in R
Lab: Multiple linear regression - model estimation in R
Lab: Multiple linear regression - prediction in R
Lab: Multiple linear regression with interaction in R
Lab: Regression with Categorical Variables: Dummy Coding Essentials in R
ANOVA - Categorical variables with more than two levels in linear regressions
GLM Preview: Logistic Regression Model & Accuracy Assessment
Compare the model accuracy (or any other metric) using thresholds of 0.1 and 0.9.
Lab: Receiver operating characteristic (ROC) curve and AUC
Your final coding exercise
This course is exactly what I was looking for to start with linear regression analysis in R. The instructor does an impressive job making students understand they need to work hard in order to learned. The examples are clear, and the explanations of the theory is very interesting.
This course is a great combination of hands-on labs and in-depth theoretical explanations as well as multiple applications and exercises. Many thanks!
This is an excellent introduction to Linear Regression. I truly think that this is the best introduction to Machine Learning here, with incredibly clear explanations. It's worth it to continue on to the other classes. Great learning curve and I loved an introduction to R that is available in the course as well.
This course really deserves 5 stars. I really enjoyed the course very much. If anybody wants to start your machine learning, this is the best course to start with.