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Understanding Decision Trees and Random Forests

Created by a Stanford alumni team, this decision trees and random forests tutorial teaches cool machine learning techniques to predict the survival probabilities aboard the Titanic!

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161 learners
Introduction to the Course:

Decision Trees are a graphic and intuitive method of predicting the outcome of a given input. They attach a weightage to the input variables and help you clearly detect what really influences your outcome. Building a Decision Tree is a tedious procedure, as they have the tendency to overfit. That's where Random Forests come into the picture. Random Forests use an ensemble of Decision Trees, this reduces the complexities without compromising on the advantages. This decision trees and random forests tutorial enhances your knowledge about the influence of these concepts in Machine Learning.

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Course Objectives

What will you gain from this course?

  • The skill to pin-point the use-cases for decision trees and random forests
  • The ability to design and apply the solution to a well known Machine Learning problem - predicting survival probabilities aboard the Titanic
  • An understanding of the danger of overfitting, and how random forests help overcome this risk

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Prerequisites and Target Audience

Knowledge of undergraduate level Mathematics will make understanding this course easier, however, it is not a prerequisite. If you would like to run the source code, you will require working knowledge of Python. This course is designed for:

  • Engineers who are interested in learning Machine Learning and applying it to solve problems
  • Big data professionals, analytics professionals, and modelers who desire to learn Machine Learning
  • Technical Executives and Investors who are excited about Machine Learning, big data, or natural language processing
  • Product Managers who desire to have intellectual conversations with data scientists about Machine Learning

Course Plan
Certificate of completion

1. Decision Fatigue and Decision Trees
10 videos
Decision Tree Algorithms 07:50

Installing Python - Anaconda and Pip 09:00

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 19:22

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

Titanic : Decision Trees predict Survival (Kaggle) - III 13:00
2. A Few Useful Things to Know About Overfitting
6 videos
Overfitting - The Bane of Machine Learning 19:04

Overfitting Continued 11:19

Cross-Validation 18:55

Simplicity is a virtue - Regularization 07:18

The Wisdom Of Crowds - Ensemble Learning 16:39

Ensemble Learning continued - Bagging, Boosting and Stacking 18:02
3. Random Forests
2 videos
Random Forests - Much more than trees 12:28

Back on the Titanic - Cross Validation and Random Forests 20:03

Meet the Author

4 Alumni of Stanford, IIM-A, IITs and Google, Microsoft, Flipkart

Loonycorn is a team of 4 people who graduated from reputed top universities. Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh have spent years (decades, actually) working in the Tech sector across the world.

  • Janani: Graduated from Stanford and has worked for 7 years at Google (New York, Singapore). She also worked at Flipkart and Microsoft.
  • Vitthal: Studied at Stanford; worked at Google (Singapore), Flipkart, Credit Suisse, and INSEAD.
  • Swetha: An IIM Ahmedabad and IIT Madras alumnus having experience of working in Flipkart.
  • Navdeep: An IIT Guwahati alumnus and Longtime Flipkart employee.
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    Ratings and Reviews     4.8/5

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