This repository presents my solutions of Coursera's "𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴" course exercices, offered by Stanford University.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
This specialization covered several topics among them :
- 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).
The course also draw from numerous case studies and applications, that also helped me to 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.