[100% OFF] Byte-Sized-Chunks: Decision Trees and Random Forests, $69

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Byte-Sized-Chunks: Decision Trees and Random Forests
Learn Intuitive Machine Learning Techniques by Exploring a Classic Problem

ACCESS
Lifetime
 
CONTENT
4.5 Hours

Decision trees and random forests are two intuitive and extremely effective Machine Learning techniques that allow you to better predict outcomes from a selected input. Both methods are commonly used in business, and knowing how to implement them can put you ahead of your peers. In this course, you'll learn these techniques by exploring a famous (but morbid) Machine Learning problem: predicting the survival of a passenger on the Titanic. 

• Access 19 lectures & 4.5 hours of content 24/7
  
• Design & implement a decision tree to predict survival probabilities aboard the Titanic
  
• Understand the risks of overfitting & how random forests help overcome them
  
• Identify the use-cases for decision trees & random forests
  
• Use provided source code to build decision trees & random forests
  

Course Outline

Decision Fatigue, And Decision Trees
You, This Course, and Us! (2:02)
Planting the seed - What are Decision Trees? (17:00)
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 (18:06)
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)

A Few Useful Things to Know About Overfitting
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 (18:03)

Random Forests
Random Forests - Much more than trees (12:28)
Back on the Titanic - Cross Validation and Random Forests (20:03)

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