# 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.

*Example course notes*

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! )