This file contains all the projects I have done through learning with Dr Andrew Ng's Machine Learning course on Coursea: https://www.coursera.org/learn/machine-learning.
The course provided a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics included:
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/variance theory, innovation process in machine learning and AI).
As part of this course, I have completed projects involving building supervised learning, unsupervised learning, deep learning and recommender system algorithms from scratch (without Scikit-learn, Tensorflow, etc) by using Octave.