1. Lessons

Here is an overview of the 16 step-by-step lessons you will complete:

• Lesson 1: Python Ecosystem for Machine Learning.
• Lesson 2: Python and SciPy Crash Course.
• Lesson 3: Load Datasets from CSV.
• Lesson 4: Understand Data With Descriptive Statistics.
• Lesson 5: Understand Data With Visualization.
• Lesson 6: Pre-Process Data.
• Lesson 7: Feature Selection.
• Lesson 8: Resampling Methods.
• Lesson 9: Algorithm Evaluation Metrics.
• Lesson 10: Spot-Check Classification Algorithms.
• Lesson 11: Spot-Check Regression Algorithms.
• Lesson 12: Model Selection.
• Lesson 13: Pipelines.
• Lesson 14: Ensemble Methods.
• Lesson 16: Model Finalization.
  

Each lesson was designed to be completed in about 30 minutes by the average developer.

2. Projects

Here is an overview of the 3 end-to-end projects you will complete:

• Project 1: Hello World Project (Iris flowers dataset).
• Project 2: Regression (Boston House Price dataset).
• Project 3: Binary Classification (Sonar dataset).
  

Each project was designed to be completed in about 60 minutes by the average developer.