What is a Decision Tree?
A Decision Tree is a flowchart like structure, where each node represents a decision, each branch represents an outcome of the decision, and each terminal node provides a prediction / label.
Why learn about Decision Trees?
- Decision Trees are the most widely and commonly used machine learning algorithms.
- Decision Trees can be used for solving both classification as well as regression problems.
- Decision Trees are robust to Outliers, so if you have Outliers in your data - you can still build Decision Tree models without worrying about impact of Outliers on your model.
- Decision Trees are easy to interpret and hence have multiple applications in different industries.
Introduction to Decision Trees - The course starts with basics of Decision Trees, the philosophy behind decision tree algorithm and why they are so popular among data scientists
Terminologies related to decision trees - Don't worry if you don't know anything about Decision Trees - that is the whole point about this course. What is a Leaf Node? Parent Node? Pruning? We will teach you all of these terminologies related to decision tree in a practical manner.
Different splitting criterion for decision tree like Gini, chi-square The course teaches you different splitting criteria like Gini, Information Gain, chi-square and how do they impact the decision tree model
Implementation of decision tree in Python - The course will tell you several best practices you should keep in mind while implementing Decision Tree algorithm
- A working laptop / desktop with 4 GB RAM
- A working Internet connection
- Basic knowledge of Machine Learning
- Basic knowledge of Python - check out this Course first, if you are new to Python
This is all it takes for you to learn one of the most powerful algorithm in Machine Learning.
What are you waiting for?
This course addresses practical challenges faced in building Decision Tree models. You will achieve these outcomes by end of this course:
- How and when to build a Decision Tree based model?
- How to ensure that your decision tree model is not overfitting the data?
- How to determine the right depth for decision tree models?
- What are some of the common interview questions related to Decision Trees.
- Which evaluation criteria should be used for splitting a decision node?
- Introduction to Decision Tree
- Quiz: Introduction to Decision Trees
- Purity in Decision Trees
- Quiz: Purity in Decision Trees
- Know Each Other
- Terminologies Related to Decision Trees
- Terminologies Related to Decision Trees
- How to Select the Best Split Point in Decision Trees
- Quiz: How to Select the Best Split Point in Decision Trees
- Quiz: Chi-Square
- Information Gain
- Quiz: Information Gain
- Reduction in Variance
- Quiz: Reduction in Variance
- Optimizing Performance of Decision Trees
- Quiz: Optimizing Performance of Decision Trees
- Decision Tree Implementation
- Dataset: Decision Tree Implementation
- Test your Skills: Decision Tree
- Where to go from here?
Pranav is a data scientist and Senior Editor for Analytics Vidhya. He has experience in data visualization and data science. Pranav has previously worked for a number of years in the learning and development field for a globally-known MNC. He brings a wealth of instructor experience to this course as he has taken multiple trainings on data science, statistics and presentation skills over the years. He is passionate about writing and has penned over 200 articles on data science for Analytics Vidhya.
I happened to cross my path on this course during casual web searching . Started the course with lot of apprehension .Content proved me wrong :-) .It was sim...Read More
I happened to cross my path on this course during casual web searching . Started the course with lot of apprehension .Content proved me wrong :-) .It was simply superb .great explanation with some practical and understandable example .Icing on cake was the ready program for the problem .Really liked it . Started one more course on the same lines from Analytics Vidhya .Read Less
Simple and well paced course that provides elegant explanation of underlying concepts!
Simple and well paced course that provides elegant explanation of underlying concepts!Read Less
I now have a good understanding of the basics of decision trees and their applications
I now have a good understanding of the basics of decision trees and their applicationsRead Less
After completion of this course, it has helped me to understand when to implement and which algorithms for Decision Tree Spllit to make a model a fit.
After completion of this course, it has helped me to understand when to implement and which algorithms for Decision Tree Spllit to make a model a fit.Read Less
Good course for beginners to get an understanding of how a decision tree works
Good course for beginners to get an understanding of how a decision tree worksRead Less
Who should take Getting Started with Decision Trees course?
This course is for people who wants to learn the most commonly used tree based algorithm: Decision Tree algorithm along with its implementation in Python.
I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?
The course assumes prior background in Machine Learning. So we would recommend you to be aware of basics of Machine Learning before going through this course.
What is the fee for this course?
This is a free course in Data Science. You only need to enrol in this free data science course and will be able to access the Decision Tree resources.
How long would I have access to "Getting started with Decision Trees" course?
Once you register, you will have 6 month access to complete the course. If you visit the course 6 month after your initial registration - you will need to enroll in the course again. Your past progress will be lost.
How much effort will this course take?
You can complete "Getting Started with Decision Trees" course in a few hours. You are also expected to apply Decision Trees and learning of this course to solve machine learning problems. The time taken in projects varies from person to person.
How can I apply and test my learnings about Decision Trees?
You can start by doing the tests at the end of various chapters. In addition, you can apply Decision Trees to solve various Practice problems on Analytics Vidhya DataHack Platform
Can I download videos from this course?
We regularly update "Getting started with Decision Trees" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos.
Which programming language is used to teach Decision Trees in this course?
This course used Python pogramming language and its open source libraries scikit-learn to teach you Decision Trees
Do I get a certificate upon completion of the course?
Yes! You will be awarded a Certificate upon completion of the course. Certificates will be awarded to those who have registered after 1 st September 2020
I just completed Decision Tree course, what should I do next?
Congratulations! We would highly recommend that you continue your machine learning journey by taking our Applied Machine Learning Course
I don't have Python Installed in my machine, what can I do?
You can go ahead and install Anaconda distribution - it will come pre-installed with everything you need including Pandas and scikit-learn libraries
How is a Free Course different from a paid course on Analytics Vidhya?
Our free courses are just the tip of the iceberg. They are good to get you started, where as paid course provide you with the depth required for industry roles.
Can I add this project on my resume and use it in my interview?
Decision Tree is one of the favourite areas for machine learning interviewers. You should go ahead and showcase your learning today.