My lecture notes and assignment solutions for the Coursera machine learning class taught by Andrew Ng.

Machine Learning Exercises from Online Course (Coursera)

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.

Implementing regularised linear regression and using it to study models with different bias-variance properties. Insights for applying machine learning.

Regularized linear regression model to predict the water flow from a dam | Examined effects of bias vs variance