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Byte-Sized-Chunks: Decision Trees and Random Forests

Janani Ravi Vitthal Srinivasan, An ex-Google, Stanford and Flipkart team

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19 Videos (4h 35m)
    • You, This Course, and Us!

      1:14
    • Planting the seed - What are Decision Trees?

      17:01
    • Growing the Tree - Decision Tree Learning

      18:03
    • Branching out - Information Gain

      18:51
    • Decision Tree Algorithms

      7:50
    • Installing Python - Anaconda and Pip

      9:00
    • Back to Basics : Numpy in Python

      18:05
    • Back to Basics : Numpy and Scipy in Python

      14:19
    • Titanic : Decision Trees predict Survival (Kaggle) - I

      19:21
    • Titanic : Decision Trees predict Survival (Kaggle) - II

      14:16
    • Titanic : Decision Trees predict Survival (Kaggle) - III

      13:00
    • Overfitting - The Bane of Machine Learning

      19:03
    • Overfitting Continued

      11:19
    • Cross-Validation

      18:55
    • Simplicity is a virtue - Regularization

      7:18
    • The Wisdom Of Crowds - Ensemble Learning

      16:39
    • Ensemble Learning continued - Bagging, Boosting and Stacking

      18:02
    • Random Forests - Much more than trees

      12:28
    • Back on the Titanic - Cross Validation and Random Forests

      20:03

About This Class

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Note: This course is a subset of our 20+ hour course 'From 0 to 1: Machine Learning & Natural Language Processing' so please don't sign up for both:-)

In an age of decision fatigue and information overload, this course is a crisp yet thorough primer on 2 great ML techniques that help cut through the noise: decision trees and random forests.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

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.

What's Covered:

  • Decision Trees are a visual and intuitive way of predicting what the outcome will be given some inputs. They assign an order of importance to the input variables that helps you see clearly what really influences your outcome.
  • Random Forests avoid overfitting: Decision trees are cool but painstaking to build - because they really tend to overfit. Random Forests to the rescue! Use an ensemble of decision trees - all the benefits of decision trees, few of the pains!
  • 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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Janani Ravi Vitthal Srinivasan

An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore, and Bangalore.

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

We think we might have hit upon a neat way of teaching ...

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