This course teaches you everything you need to create a decision tree/ random forest/ XGBoost model in R and covers all the steps that you should take to solve a business problem through a decision tree.

Below are the course contents of this course on linear regression:

Section 1 - Introduction to machine learning

In this section, we will learn what machine learning means. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning is. It also contains steps involved in building a machine-learning model, not just linear models, any machine-learning model.

Section 2 - R basic

This section will help you to set up R and R studio on your system and it'll teach you how to perform some basic operations in R.

Section 3 - Pre-processing and simple decision trees

In this section you will learn what actions you need to take to prepare it for analysis; these steps are very important for creating something meaningful. we will start with the basic theory of decision trees then cover data pre-processing topics like missing value imputation, variable transformation, and test-train split. In the end, we will create and Description a simple regression decision tree.

Section 4 - Simple classification tree

In this section we will expand our knowledge of regression decision trees to classification trees, we will also learn how to create a classification tree in Python

Section 5, 6 and 7 - Ensemble technique

In this section we will start our discussion about advanced ensemble techniques for decision trees. Ensembles techniques are used to improve the stability and accuracy of machine-learning algorithms. In this course we will discuss random forest, bagging, gradient boosting, AdaBoost and XGBoost.

By the end of this course, your confidence in creating a decision tree model in R will soar. You'll have a thorough understanding of how to use decision tree modeling to create predictive models and solve business problems.

All the codes and supporting files for this course are available at -