Decision Tree Classifier - Introduction & Python code explained | Machine Learning Algorithm basics

A Decision Tree multi-class classifier in Python (using the scikit-learn machine learning library) is clearly introduced and explained in this video, including a line-by-line explanation of the Python code. Decision Tree machine learning algorithm is a very useful technique and after watching this video you will be able to use this method in your classification projects.

Topics covered in this video:
1. Introduction to Decision Trees
2. Classification using Decision Tree
3. Steps to program a Decision Tree classifier
4. Entropy in Decision Trees explained
5. Advantages and disadvantages of Decision Trees
6. Python code for Decision Tree classifier
7. Visualization of Decision Trees

Decision Tree algorithm belongs to the family of supervised learning algorithms. The goal of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data (training data).

Chapters:
0:00 Introduction
0:19 DT Basics
1:13 Classification Task
1:32 Dataset explanation
1:52 Steps for coding
2:46 Entropy in Decision Tree
3:30 Advantages & Disadvantages
4:24 Python Code
4:47 Import Dataset
5:22 Data Pre-processing
6:13 Define Features & Labels
6:42 Train Test Split
7:46 Modelling Decision Tree
8:45 Accuracy of the Model
9:00 Visualization Code
9:18 Visualization Explained

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