/Ng_ML

Andrew Ng's machine learning course implemented in Python

Primary LanguageJupyter Notebook

Machine learning

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.