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