I completed Andrew Ng's/Stanford University's machine learning course on Coursera, but instead of using the Matlab templates provided by the course, I implemented everything from scratch in Python.

Contents:

• Ex1: Linear regression

  With one variable and with multiple variables.

• Ex2: Logistic regression

  Including regularization.

• Ex3: Multi-class classification and neural networks

  Handwritten number recognition using two different methods: one-vs-all logistic regression, and forward propagating a pre-trained neural network.

• Ex4: Neural networks learning

  Build and train a neural network, including backpropagation, and use it for handwritten number recognition.

• Ex5: Regularized linear regression and bias vs variance

  Including learning curves and polynomial regression.

• Ex6: Support Vector Machines

  6.1: Demonstrate scikit-learn SVMs with linear and Gaussian kernels on some sample 2D datasets.

  6.2: Build an email spam classifier using natural language processing and a scikit-learn SVM.

• Ex7: K-means Clustering and Principal Component Analysis

  7.1: Build a k-means clustering algorithm and use it for image compression.

  7.2: Build a PCA algorithm and use it for image compression and for visualisation.

• Ex8: Anomaly Detection and Recommender Systems

  8.1: Anomaly detection using a multivariate Gaussian model. Precision, recall, F1 score.

  8.2: Build a recommender system and use it to recommend movies.