Machine learning : decision trees & random forests online course

Course Outline

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

PACKAGE INCLUDES:

Length of Subscription: 12 Months Online On-Demand Access
Running Time: 4 hrs 50 min
Platform: Windows & MAC OS
Level: Beginner to Advanced
Project Files: Included

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