Decision Trees for Machine Learning From Scratch

Decision Trees for Machine Learning From Scratch, available at $44.99, has an average rating of 4.05, with 21 lectures, based on 86 reviews, and has 408 subscribers.

You will learn about The most common decision tree algorithms Understand the core idea behind decision trees Developing code from scratch Applying ML for practical problems Bagging and Boosting Random Forest, Gradient Boosting This course is ideal for individuals who are Interested in Machine Learning or Wonder Data Mining It is particularly useful for Interested in Machine Learning or Wonder Data Mining.

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Summary

Title: Decision Trees for Machine Learning From Scratch

Price: $44.99

Average Rating: 4.05

Number of Lectures: 21

Number of Published Lectures: 21

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

The most common decision tree algorithms
Understand the core idea behind decision trees
Developing code from scratch
Applying ML for practical problems
Bagging and Boosting
Random Forest, Gradient Boosting

Who Should Attend

Interested in Machine Learning
Wonder Data Mining

Target Audiences

Interested in Machine Learning
Wonder Data Mining

Decision trees are one of the hottest topics in Machine Learning. They dominate many Kaggle competitions nowadays. Empower yourself for challenges.

This course covers both fundamentals of decision tree algorithms such as CHAID, ID3, C4.5, CART, Regression Trees and its hands-on practical applications. Besides, we will mention some bagging and boosting methods such as Random Forest or Gradient Boosting to increase decision tree accuracy. Finally, we will focus on some tree based frameworks such as LightGBM, XGBoost and Chefboost.

We will create our own decision tree framework from scratch in Python. Meanwhile, step by step exercises guide you to understand concepts clearly.

This course appeals to ones who interested in Machine Learning, Data Science and Data Mining.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: ID3 Decision Tree Algorithm

Lecture 1: ID3 Overview

Lecture 2: Entropy calculation

Lecture 3: Information Gain

Lecture 4: Iterative Dichotomiser

Lecture 5: Extending ID3

Chapter 3: C4.5 Decision Tree Algorithm

Lecture 1: C4.5 Overview

Lecture 2: Gain Ratio Calculation

Lecture 3: Handling with Continuous Features

Lecture 4: Extending C4.5

Lecture 5: Transforming decision rules to python if statements

Chapter 4: Classification and Regression Trees (CART)

Lecture 1: CART overview

Lecture 2: CART for classification

Lecture 3: Regression Trees Overview

Lecture 4: Regression Trees

Chapter 5: CHAID Decision Trees

Lecture 1: CHAID Decision Trees Overview

Chapter 6: Random Forest

Lecture 1: Random Forest

Chapter 7: Gradient Boosting Machines

Lecture 1: Introduction to GBM

Chapter 8: Decision Tree Based Frameworks

Lecture 1: LightGBM

Lecture 2: XGBoost

Lecture 3: LightGBM vs XGBoost

Instructors

Sefik Ilkin Serengil
Software Engineer

Rating Distribution

1 stars: 0 votes
2 stars: 7 votes
3 stars: 10 votes
4 stars: 13 votes
5 stars: 56 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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