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Supervised Machine Learning: Regression and Classification

Week 1

Overview of Machine Learning

  1. Video: Welcome to machine learning! - 2 min
  2. Video: Applications of machine learning - 4 min

Supervised vs. Unsupervised Machine Learning

  1. Video: What is machine learning? - 5 min
  2. Video: Supervised learning part 1 - 6 min
  3. Video: Supervised learning part 2 - 7 min
  4. Video: Unsupervised learning part 1 - 8 min
  5. Video: Unsupervised learning part 2 - 3 min
  6. Video: Jupyter Notebooks - 4 min
  7. Lab: Python and Jupyter Notebooks

Regression Model

  1. Video: Linear regression model part 1 - 10 min
  2. Video: Linear regression model part 2 - 6 min
  3. Lab: Model representation
  4. Video: Cost function formula - 9 min
  5. Video: Cost function intuition - 15 min
  6. Video: Visualizing the cost function - 8 min
  7. Video: Visualization examples - 6 min
  8. Lab: Cost function

Train the model with gradient descent

  1. Video: Gradient descent - 8 min
  2. Video: Implementing gradient descent - 9 min
  3. Video: Gradient descent intuition - 7 min
  4. Video: Learning rate - 9 min
  5. Video: Gradient descent for linear regression - 6 min
  6. Video: Running gradient descent - 5 min
  7. Lab: Gradient descent

Week 2

Multiple linear regression

Video: Multiple features - 9 min Video: Vectorization part 1 - 6 min Video: Vectorization part 2 - 6 min Lab: Python, NumPy and vectorization Video: Gradient descent for multiple linear regression - 7 min Lab: Multiple linear regression

Gradient descent in practice

Video: Feature scaling part 1 - 6 min Video: Feature scaling part 2 - 7 min Video: Checking gradient descent for convergence - 5 min Video: Choosing the learning rate - 6 min Lab: Feature scaling and learning rate Video: Feature engineering - 3 min Video: Polynomial regression - 5 min Lab: Feature engineering and Polynomial regression Lab: Linear regression with scikit-learn

Practice quiz: Gradient descent in practice Week 2 practice lab: Linear regression Programming Assignment: Week 2 practice lab: Linear regression 3 hours3h

Week 3

Classification with logistic regression

  1. Video: Motivations - 9 min
  2. Lab: Classification
  3. Video: Logistic regression - 9 min
  4. Lab: Sigmoid function and logistic regression
  5. Video: Decision boundary - 10 min
  6. Lab: Decision boundary

Cost function for logistic regression

  1. Video: Cost function for logistic regression - 11 min
  2. Lab: Logistic loss
  3. Video: Simplified Cost Function for Logistic Regression - 5 min
  4. Lab: Cost function for logistic regression

Gradient descent for logistic regression

  1. Video: Gradient Descent Implementation 6 min
  2. Lab: Gradient descent for logistic regression
  3. Lab: Logistic regression with scikit-learn

The problem of overfitting

  1. Video: The problem of overfitting - 11 min
  2. Video: Addressing overfitting - 8 min
  3. Lab: Overfitting
  4. Video: Cost function with regularization - 9 min
  5. Video: Regularized linear regression - 8 min
  6. Video: Regularized logistic regression - 5 min
  7. Lab: Regularization

Week 3 practice lab: logistic regression Programming Assignment: Week 3 practice lab: logistic regression 3 hours3h