The following files are solutions to the online Coursera course 'Machine Learning' by Andrew Ng. The exercise description is provided in PDF format and the solutions are provided in both Pythng and MATLAB. All corresponding data files are also provided.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Exercise 1 - Linear Regression
Python (Jupyter)
MATLAB
Exercise PDF
Exercise 2 - Logistic Regression Python (Jupyter) MATLAB Exercise PDF
Exercise 3 - Multi-class Classification and Neural Networks Python (Jupyter) MATLAB Exercise PDF
Exercise 4 - Neural Networks Learning Python (Jupyter Notebook) MATLAB Exercise PDF
Exercise 5 - Regularized Linear Regression and Bias v.s. Variance Python (Jupyter) MATLAB Exercise PDF
Exercise 6 - Support Vector Machines Python (Jupyter) MATLAB Exercise PDF
Exercise 7 - K-means Clustering and Principal Component Analysis Python (Jupyter) MATLAB Exercise PDF
Exercise 8 - Anomaly Detection and Recommender Systems Python (Jupyter) MATLAB Exercise PDF
https://www.coursera.org/learn/machine-learning/home/welcome