courseAI Machine Learning – Decision Trees & Random Forests 2017

Learn Intuitive Machine Learning Techniques by Exploring a Classic Problem.

In an age of decision fatigue and information overload, this “Machine Learning: Decision Trees & Random Forests” course is a crisp yet thorough primer on two great Machine Learning techniques that help cut through the noise: decision trees and random forests.

Design and Implement the solution to a famous problem in machine learning: predicting survival probabilities aboard the Titanic. Understand the perils of overfitting, and how random forests help overcome this risk. Identify the use-cases for Decision Trees as well as Random Forests.

No prerequisites required, but knowledge of some undergraduate level mathematics would help, but is not mandatory. Working knowledge of Python would be helpful if you want to perform the coding exercise and understand the provided source code.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

Python Activity: Surviving aboard the Titanic! Build a decision tree to predict the survival of a passenger on the Titanic. This is a challenge posed by Kaggle (a competitive online data science community). We’ll start off by exploring the data and transforming the data into feature vectors that can be fed to a Decision Tree Classifier.


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Great presentation!

What the course will teach you:

  • Planting the seed: What are Decision Trees?
  • Growing the Tree: Decision Tree Learning
  • Branching out: Information Gain
  • Decision Tree Algorithms
  • Installing Python: Anaconda & PIP
  • Back to Basics: Numpy & Scipy in Python
  • Much More..

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AI Machine Learning – Decision Trees & Random Forests

Course Length: 5 Hours

Chapter 01: Decision Fatigue & Decision Trees

Lesson 01: Introduction: You, This Course & Us!
Lesson 02: Planting the seed: What are Decision Trees?
Lesson 03: Growing the Tree: Decision Tree Learning
Lesson 04: Branching out: Information Gain
Lesson 05: Decision Tree Algorithms
Lesson 06: Installing Python: Anaconda & PIP
Lesson 07: Back to Basics: Numpy in Python
Lesson 08: Back to Basics: Numpy & Scipy in Python
Lesson 09: Titanic: Decision Trees predict Survival (Kaggle) – I
Lesson 10: Titanic: Decision Trees predict Survival (Kaggle) – II
Lesson 11: Titanic: Decision Trees predict Survival (Kaggle) – III

Chapter 02: A Few Useful Things to Know about Overfitting

Lesson 01: Overfitting: The Bane of Machine Learning
Lesson 02: Overfitting continued
Lesson 03: Cross-Validation
Lesson 04: Simplicity is a virtue: Regularization
Lesson 05: The Wisdom of Crowds: Ensemble Learning
Lesson 06: Ensemble Learning continued: Bagging, Boosting & Stacking

Chapter 03: Random Forests

Lesson 01: Random Forests: Much more than trees
Lesson 02: Back on the Titanic: Cross Validation & Random Forests

System Requirements

Minimum specifications for the computer are:


Microsoft Windows XP, or later
Modern and up to date Browser (Internet Explorer 8 or later, Firefox, Chrome, Safari)


OSX/iOS 6 or later
Modern and up to date Browser (Firefox, Chrome, Safari)

All systems:

Internet bandwidth of 1Mb or faster
Flash player or a browser with HTML5 video capabilities (We recommend Google Chrome)