Machine Learning with Python Course

01 Let’s Discuss Learning 8 topics +

• Welcome
• Scope, Terminology, Prediction, and Data
• Putting the Machine in Machine Learning
• Examples of Learning Systems
• Evaluating Learning Systems
• A Process for Building Learning Systems
• Assumptions and Reality of Learning
• End-of-Lesson Material

02 Some Technical Background 11 topics +

• About Our Setup
• The Need for Mathematical Language
• Our Software for Tackling Machine Learning
• Probability
• Linear Combinations, Weighted Sums, and Dot Products
• A Geometric View: Points in Space
• Notation and the Plus-One Trick
• Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
• NumPy versus “All the Maths”
• Floating-Point Issues
• EOC

03 Predicting Categories: Getting Started with Classification 8 topics +

• Classification Tasks
• A Simple Classification Dataset
• Training and Testing: Don’t Teach to the Test
• Evaluation: Grading the Exam
• Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
• Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
• Simplistic Evaluation of Classifiers
• EOC

04 Predicting Numerical Values: Getting Started with Regression 6 topics +

• A Simple Regression Dataset
• Nearest-Neighbors Regression and Summary Statistics
• Linear Regression and Errors
• Optimization: Picking the Best Answer
• Simple Evaluation and Comparison of Regressors
• EOC

05 Evaluating and Comparing Learners 9 topics +

• Evaluation and Why Less Is More
• Terminology for Learning Phases
• Major Tom, There’s Something Wrong: Overfitting and Underfitting
• From Errors to Costs
• (Re)Sampling: Making More from Less
• Break-It-Down: Deconstructing Error into Bias and Variance
• Graphical Evaluation and Comparison
• Comparing Learners with Cross-Validation
• EOC