Byte-Sized-Chunks: Decision Trees and Random Forests

Cool machine learning techniques to predict survival probabilities aboard the Titanic - a Kaggle problem!

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.

What you’ll learn

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

Are there any course requirements or 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 perform the coding exercise and understand the provided source code

Who this course is for:

Nope! Please don't enroll for this class if you have already enrolled for our 21-hour course 'From 0 to 1: Machine Learning and NLP in Python'
Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing