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?
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Decision Trees are the most widely and commonly used machine learning algorithms.
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It can be used for solving both classification as well as regression problems.
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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?
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Introduction to Decision Trees
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Terminologies related to decision trees
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Different splitting criterion for decision tree like Gini, chi-square, etc.
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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
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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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