First, learn foundations. Linear algebra, vector calculus, probability, statistics. Try going through Kevin Murphy's books. They're relatively self contained. If you reach a dependency that they don't cover, pick up a textbook on it.

I'd recommend you take a look at course.fast.ai at some point (the earlier the better, perhaps?).

I love Jeremy Howard's "top-down" approach, whole almost every other course I've learned is bottom-up. To elaborate, the fast.ai course begins with implementing a classification model and deploying it to the web, and then unpacks everything you did and why it worked. Like unpeeling an onion layer by layer.

It has the benefit of the immediate reward of seeing something work, and you can make it work for something you find useful (e.g. maybe it's useful to you to know which images on your phone have people in the foreground vs which do not?). Once you've made something useful, you can let your curiosity drive you - why did this work, how did this work, what would I have do to improve this a certain way, etc.

Consider if this approach works for you.

For a bottom-up approach (or as a follow-up deep dive into the fast.ai course), I would say the book Deep Learning by Goodfellow, Bengio, Courville is a good resource. It's available for free at deeplearningbook.org.

There are plenty more resources out there. But I tend to feel overwhelmed by this fact, more than helped.

I would just add that for a more textbook approach, consider Pattern recognition & Machine Learning by Bishop (now available as a free PDF apparently). I personally used it while studying Machine Learning, and it does a really, really good job of listing exercises (and solutions to most) for you to test your knowledge.

seems like this is better suited to /r/learnmachinelearning

Great Suggestion, i will definitely have a look. One question though, are these fields interlinked i mean, deep learning, data science, machine learning. Or these are stacked on each other?

50 textbooks later...

Just work for about 10 years in the field, while making substancial contributions to it. Then you can call yourself an expert

http://neuralnetworksanddeeplearning.com/

This is all you need. After that, start reading papers.

Deep neural networks are a specific type of statistical model that is basically made up of repeatedly stapled together basic statistical models. This makes statistics the broadest field.

Essentially all machine learning methods come from probability, statistics, and signal processing.

Machine learning contains other methods than deep neural networks, but they are not as popular and I wouldn't worry about them at first.

Understand linear regression, probability and statistics first, then try to understand neural networks. Focus on statistical modeling, not null hypothesis significance testing.

Linear algebra can be skipped IMO.

Fully agree with everything but Murphy’s book. I enjoy it as a reference, but it’s a poor initial introduction to ML as I found it needs some external “structure” to link ideas across chapters.

Thanks but I am confused about the fields, are neural networks and machine learning same or different fields.

Neural networks are a form of machine learning, and they are what people refer to when the say "AI".

ok, as others are saying that I should have a good command in Statistics and linear regression so as a senior what will be your suggestion, should I revise it before diving deep or should I learn each concept when I stumbled onto it?