Stanford Machine Learning: Intro

I have decided to take part in the machine elarning courses provided by Stanford University.

Now there are loads of MOOCs but this course was  one of the first programming MOOCs Coursera put online by Coursera and it is still ranked as first by Class Central.

I have now almost completed the 11 weeks course and I can tell that Stanford Professor Andrew Ng is a brillant teacher, he is able to explain quite complicated algorithm in a very simple way.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.

Topics include:

Supervised learning (parametric/non-parametric algorithms, linear regression, logistic regression, 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).

I have some background in maths and computer science but even without math background I am sure it will not be too challenging as Prof Ng simplifies ML as much as possible.

The only prerequisites that you need is a sufficient level in computer programming and a hich school level in math. (Linear Algebra and Statistics).

I will try to update this post soon to give you more details about this course and I’ll a create a new post for each week.