Free Online Diploma in Machine Learning with R Course

This free online Machine Learning in R course can help you launch a flourishing career in the field of Data Science & Machine Learning. This course covers the basic ML models such as Linear & Logistic regression and the advanced models such as Decision trees, SVM, XGBOOST, Forests etc. By the end of this course you will be able to confidently build predictive Machine Learning models to solve business problems as well as create business strategy.

What You Will Learn In This Free Course

Introduction to Machine Learning with R studio

Welcome to this course about Machine Learning with R studio! Find out what is covered in the entire course in this module.; Module

Setting Up R studio and R Crash course

In this Module, we help you set-up the R and R studio environment and teach you some basic statistical operations in R.; Module

Basics of Statistics

In this Module, we cover some basic statistical terminologies such as Mean Median and Mode. If you are familiar with these, you can skip this module.; Module

Introduction to Machine Learning

In this module, we discuss about the common terms associated with machine learning, look at some practical applications of ML and understand the process involved in creating an ML model; Module

Data Preprocessing and Preparation

The first and most important step in building any Machine Learning Model is collecting and pre-processing Data. Learn how to get the data ready before you build any model. This step is critical and this is what separates a great model from other models.

Linear Regression Model

Learn how to build linear regression models, test-train split and how to assess the accuracy of the model; Module

Regression Models other than OLS

Learn other regression models, like Lasso, Ridge regression and subset selection which help reduce overfitting.; Module

Data Preparation

Prepare the data for building classification models. This is a critical step before building the model as the quality of our model depends on it.; Module

The Three Classification Models

This module discusses the problem statement that we will be solving using three types of classification ML models; Module

Logistic Regression

In this module we learn how to make a Logistic regression model which is one the most popular classical ML technique for classification.; Module

Linear Discriminant Analysis

In this module, we learn about linear discriminant analysis which is commonly used in Marketing Analytics.; Module

K-Nearest Neighbors

In this module, we learn how to create a KNN model, which is a non-parametric technique for classification.; Module

Comparing Results from 3 Models

In this module, we compare the outputs from the three techniques to understand which works well for what kind of problem statement.; Module

Diploma in Machine Learning with R Studio – First Assessment

Test your understanding of Linear Regression, Logistic regression, LDA and K-NN; Module

Simple Decision Trees

In this module, we understand the basics of a decision tree and build a simple regression decision tree.; Module

Simple Classification Tree

In this module, we learn how to make a classification decision tree.; Module

Ensemble Techniques

In this module, we explore some really advanced ML models. We learn about Bagging, Random Forest and Boosting techniques. In fact, XGBoost has won several Kaggle competitions.; Module

Concept

Understand the theoretical concepts behind SVM and what makes this technique different from other ML techniques.; Module

Creating Support Vector Machine Model in R

In this module we will create the different SVM models in R studio.; Module

Diploma in Machine Learning with R Studio – Second Assessment

Test your understanding of the concepts of Decision Trees, Ensemble methods and SVM model; Module

Conclusion

Thank you for staying with us throughout the course.; Module

Course assessment