Machine Learning – Decision Trees & Random Forests


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.   Supplemental Material included!

Length: 4 hrs 50 min

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This Machine Learning: Decision Trees & Random Forests online course will teach you cool machine learning techniques to predict survival probabilities aboard the Titanic – a Kaggle problem!

Course Highlights:

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.

Who is the target audience?

  • Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
  • Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Tech executives and investors who are interested in big data, machine learning or natural language processing

Sample clip


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

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum