M0. Welcome to Introduction to Machine Learning
0. Welcome!
1. Introduction to Machine Learning
1.1. Prerequisite confirmation
M1. Machine Learning Terminology
0. Module Learning Outcomes
1. What is Supervised Machine Learning?
1.1. Exercises
2. Types of Machine Learning
2.1. Exercises
3. Classification vs. Regression
3.1. Exercises
4. Tabular Data and Terminology
4.1. Exercises
5. Baselines: Training a Model using Scikit-learn
5.1. Exercises
6. Baselines: Dummy Regression
6.1. Exercises
7. What Did We Just Learn?
M2. Decision Trees
0. Module Learning Outcomes
1. Introducing Decision Trees
1.1. Exercises
2. Building a Decision Tree Classifier
2.1. Exercises
3. Decision Trees with Continuous Features
3.1. Exercises
4. Parameters and Hyperparameters
4.1. Exercises
5. Decision Tree Regressor
5.1. Exercises
6. Generalization
6.1. Exercises
7. What Did We Just Learn?
M3. Splitting, Cross-Validation and the Fundamental Tradeoff
0. Module Learning Outcomes
1. Splitting Data
1.1. Exercises
2. Train, Validation and Test Split
2.1. Exercises
3. Cross Validation
3.1. Exercises
4. Underfitting and Overfitting
4.1. Exercises
5. Fundamental Tradeoff and the Golden Rule
5.1. Exercises
6. What Did We Just Learn?
M4.Similarity-Based Approaches to Supervised Learning
0. Module Learning Outcomes
1. Terminology
1.1. Exercises
2. Distances
2.1. Exercises
3. Finding the Nearest Neighbour
3.1. Exercises
4. π-Nearest Neighbours (π-NNs) Classifier
4.1. Exercises
5. Choosing π (n_neighbors)
5.1. Exercises
6. π -Nearest Neighbours Regressor
6.1. Exercises
7. Support Vector Machines (SVMs) with RBF Kernel
7.1. Exercises
8. What Did We Just Learn?
M5. Preprocessing Numerical Features, Pipelines and Hyperparameter Optimization
0. Module Learning Outcomes
1. The Importance of Preprocessing
1.1. Exercises
2. Case Study: Preprocessing with Imputation
2.1. Exercises
3. Case Study: Preprocessing with Scaling
3.1. Exercises
4. Case Study: Pipelines
4.1. Exercises
5. Automated Hyperparameter Optimization
5.1. Exercises
6. What Did We Just Learn?
M6. Preprocessing Categorical Variables
0. Module Learning Outcomes
1. Categorical Variables: Ordinal Encoding
1.1. Exercises
2. Categorical Variables: One-Hot Encoding
2.1. Exercises
3. ColumnTransformer
3.1. Exercises
4. Make - Pipelines & Column Transformers
4.1. Exercises
5. Handeling Categorical Features: Binary, Ordinal and More
5.1. Exercises
6. Text Data
6.1. Exercises
7. What Did We Just Learn?
M7. Assessment and Measurements
0. Module Learning Outcomes
1. Introducing Evaluation Metrics
1.1. Exercises
2. Precision, Recall and F1 Score
2.1. Exercises
3. Multi-Class Measurements
3.1. Exercises
4. Imbalanced Datasets
4.1. Exercises
5. Regression Measurements
5.1. Exercises
6. Passing Different Scoring Methods
6.1. Exercises
7. What Did We Just Learn?
M8. Linear Models
0. Module Learning Outcomes
1. Introducing Linear Regression
1.1. Exercises
2. Coefficients and coef_
2.1. Exercises
3. Logistic Regression
3.1. Exercises
4. Predicting Probabilities
4.1. Exercises
5. Multi-class Classification
5.1. Exercises
6. What Did We Just Learn?
Module Closing Remarks
0. Congratulations!
1.1. Exercises
Questions on Why
Motivation True and False
Preprocessing Questions
1. The Importance of Preprocessing
2. Case Study: Preprocessing with Imputation