And I’m surprised to not find Aurelion Geron’s absolute masterpiece listed below. I believe it is the best machine learning book ever, although Statistical Learning mentioned in the article is really good as well :
If no, what are the great resources for starters?
The videos / slides / assignments from here:
Any tips before I get this journey going?
Depending on your maths background, you may need to refresh some math skills, or learn some new ones. The basic maths you need includes calculus (including multi-variable calc / partial derivatives), probability / statistics, and linear algebra. For a much deeper discussion of this topic, see this recent HN thread:
Luckily there are tons of free resources available online for learning various maths topics. Khan Academy isn't a bad place to start if you need that. There are also tons of good videos on Youtube from Gilbert Strang, Professor Leonard, 3blue1brown, etc.
Also, check out Kaggle.com. Doing Kaggle contests can be a good way to get your feet wet.
And the various Wikipedia pages on AI/ML topics can be pretty useful as well.
* Book: Hands-On Machine Learning w/ Scikit-Learn & TensorFlow (http://amzn.to/2vPG3Ur). Theory & code, starting from "shallow" learning (eg Linear Regression) on sckikit-learn, pandas, numpy; and moves to deep learning with TF.
* Podcast: Machine Learning Guide (http://ocdevel.com/podcasts/machine-learning). Commute/exercise backdrop to solidify theory. Provides curriculum & resources.
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