Getting Started with Decision Trees

Getting Started with Decision Trees, available at $19.99, has an average rating of 4.1, with 11 lectures, 8 quizzes, based on 33 reviews, and has 2680 subscribers.

You will learn about Basics of Decision Trees How to Apply Decision Trees to build Machine Learning models Building Decision Tree models in Python How to improve and optimize your decision tree models This course is ideal for individuals who are Beginner Data Science Learners or Python Developers It is particularly useful for Beginner Data Science Learners or Python Developers.

Enroll now: Getting Started with Decision Trees

Summary

Title: Getting Started with Decision Trees

Price: $19.99

Average Rating: 4.1

Number of Lectures: 11

Number of Quizzes: 8

Number of Published Lectures: 11

Number of Published Quizzes: 8

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: ₹6,500

Quality Status: approved

Status: Live

What You Will Learn

Basics of Decision Trees
How to Apply Decision Trees to build Machine Learning models
Building Decision Tree models in Python
How to improve and optimize your decision tree models

Who Should Attend

Beginner Data Science Learners
Python Developers

Target Audiences

Beginner Data Science Learners
Python Developers

Decision Tree algorithm is one of the most powerful algorithms in machine learning and data science. It is very commonly used by data scientists and machine learning engineers to solve business problem and explain that to your customers easily. This course will introduce you to the concept of Decision Trees and teach you how to build one using Python

Why learn about Decision Trees?

Decision Trees are the most widely and commonly used machine learning algorithms.
It can be used for solving both classification as well as regression problems.
Decision Trees are easy to interpret and hence have multiple applications around different industries.

What would you learn in Getting started with Decision Tree course?

Introduction to Decision Trees
Terminologies related to decision trees
Different splitting criterion for decision tree like Gini, chi-square, etc.
Implementation of decision tree in Python

Course Curriculum

Chapter 1: Getting Started with Decision Tree

Lecture 1: Introduction to Decision Tree

Lecture 2: Purity in Decision Trees

Lecture 3: Terminologies Related to Decision Trees

Lecture 4: How to Select the Best Split Point in Decision Trees

Lecture 5: Chi-Square

Lecture 6: Information Gain

Lecture 7: Reduction in Variance

Lecture 8: Optimizing Performance of Decision Trees

Lecture 9: Decision Tree Implementation

Lecture 10: Dataset: Decision Tree Implementation

Lecture 11: Bonus Lecture

Instructors

Analytics Vidhya
Data Science Community

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 4 votes
4 stars: 12 votes
5 stars: 15 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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