Ian Goodfellow’s Deep Learning book pretty much useless. I own it and have read through most parts of it. I couldn’t explain it better than top Amazon reviews:

https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

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 :

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

Wow, this is a lot more in-depth than I was expecting. I started out with the O'Reilly book (Hands-on Machine Learning)[1]. There's some overlap there, but I'd say both this course and the O'Reilly book are well worth your time if you're starting out with machine learning. [1] https://amzn.to/2kKaNiQ

I think this is the same author that published "Hands-On Machine Learning with Scikit-Learn and TensorFlow...". The quality of the book (thus far) is so high that I immediately started Googling about the author to try and learn more (and did not learn much), assuming he must be well known. I did not learn much, but can at least say the book is fantastic.

[1]: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

If TensorFlow is what you're interested in I personally found "Hands-on Machine Learning with SciKit-Learn and TensorFlow by Aurélien Géron" to be the best introduction after introducing myself to the subject with Siraj's YouTube videos

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

What? No. Why in the world do people even ask this kind of question. To a first approximation, the answer to "is it too late to get started with ..." question is always "no".

If no, what are the great resources for starters?

The videos / slides / assignments from here:

http://ai.berkeley.edu/home.html

This class:

https://www.coursera.org/learn/machine-learning

This class:

https://www.udacity.com/course/intro-to-machine-learning--ud...

This book:

https://www.amazon.com/Artificial-Intelligence-Modern-Approa...

This book:

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

This book:

https://www.amazon.com/Introduction-Machine-Learning-Python-...

These books:

http://greenteapress.com/thinkstats/thinkstats.pdf

http://www.greenteapress.com/thinkbayes/thinkbayes.pdf

This book:

https://www.amazon.com/Machine-Learning-Hackers-Studies-Algo...

This book:

https://www.amazon.com/Thoughtful-Machine-Learning-Test-Driv...

These subreddits:

http://artificial.reddit.com

http://machinelearning.reddit.com

http://semanticweb.reddit.com

These journals:

http://www.jmlr.org

http://www.jair.org

This site:

http://arxiv.org/corr/home/

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:

https://news.ycombinator.com/item?id=15116379

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.

* Course: fast.ai (http://course.fast.ai). Practical, to the point, theory + code.

* 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.