Machine Learning course review

I have been meaning to write a review of this course for a long time. This course is actually very special to me as it was the first MOOC that I ever attended. It is also one of the first few courses that laid the foundation for Coursera. It is taught by Professor Andrew Ng, director of Stanford’s AI lab and co-founder of Coursera.

The course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. It is a great course for a first introduction to Machine Learning. At a high level, it covers the following topics:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)
  • Unsupervised learning (clustering, dimensionality reduction, recommendation systems, deep learning)
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)

It also discusses several case studies and applications, including building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

This is a 10 weeks course. Each week a set of videos with embedded in-video questions, a quiz and a programming assignment are released. The course focuses on the practical applications of machine learning rather than the mathematical theory behind it. The programming assignments are very thorough and cover almost all the topics taught in the class : Regression, Spam Classification, Neural Networks, SVMs, Dimensionality reduction and Recommendation systems. All coding is done in Octave.

This is a hectic course which will keep you engaged throughout. I would recommend it for everyone serious about Computer Science.

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