Where do you start?

I’ve been keeping my eye on the market for Data Scientists and, with my maths and software engineering background, thinking that it’s something I could get into. However, it’s over 25 years since my maths degree - so will need a thorough refresher on matrix maths and probability theory … so where do you start?

After a good deal of searching around, I found this excellent course by Andrew Ng as part of the Coursera Standford University catalogue.

Coursera Machine Learning by Stanford University

First principles

Andrew introduces machine learning from the ground up - covering Linear Regression, Logistic Regression, Cost Functions, Gradient Descent, Feed Forward Neural Network, etc..

Andrew also includes how to use the Octave programming environment: Octave is used throughout the course for practical elements and you can only progress once you’ve completed your homework in Octave and sent it for assessment.

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Example course notes

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There’s extensive course videos, accompanying slides, additional notes, and a tutor group forum. A lot of effort has gone into this course … and (unbelievably) it’s free!

Time commitment

You’ll need to dedicate a fair chunk of time to completing this course - especially if you’re rusty like me. But it’s well worth it.

Good luck !

Other resources

You can use Towards Data Science as a jump-off point for hundreds of articles and other resources - it’s a great starting point for your searches.

And this book is a comprehensive practical guide you should find useful: Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition

(Update Dec 2018: - take a look at Google’s “Crash Course” in machine learning - it’s very good! )

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