What are some resources to improve practical expertise in machine learning? I studied machine learning when I was in university, a couple of years ago. I used it for my master thesis (decision trees, ensembles, SVM, and word embedding mostly) and for other projects either personal and academics (genetic algorithms, Q-learning).
Then I studied neural networks by myself, from the pure theory of the perceptron, backpropagation, gradient descent, etc. (with Bengio's deep learning book) to the various ramifications of ConvNets, DeConvNets, auto-encoders, LSTM, deep reinforcement learning, and other various papers. I took a several months break and there are already new types of networks that I don't know, so hard to keep up with the state-of-the-art.
However, my knowledge is purely theoretical. I want to embed machine learning in my future career, but getting a job in the field requires practical expertise, which I lack.
I am working full time now as a software engineer. So, what do you think may be a possible path to follow in order to gain expertise? Given that I have a strong mathematical background and more than 10 years of coding on my back, is there any resource on practical rules of thumbs, suggestions, real case studies, etc., which I can invest time onto? I mean something beyond the vanilla &quot;introduction to machine learning&quot;, something more real and concrete.
Do you think that attempting Kaggle challenges from scratch would be stimulating? Or too confusing? Do you think delivering a personal project (i.e. not a byproduct of my current job) in which I employ one such method to solve a potentially real problem would be enough to be relevant in my cv?
Since you are a software engineer, you already have some experience with one or more programming languages, so this is already a good thing. Moreover, you say that you have a strong mathematical background, so you will probably learn certain machine learning concepts more easily.
When it comes to machine learning, there are open source projects that you can contribute to via Github, and this really boosts your skills and experience.
I also am contributing to an open-source conversation agent via Github.
If you are able to understand and contribute to such projects, then implementing ML/DL algorithms is possible in your target language. I myself have been using Python, but that's just a personal choice, and it's been utilized by many AI engineers and data scientists, globally.

Do you think that attempting kaggle challenges from scratch would be
stimulating? Or too confusing? Do you think delivering a personal
project (i.e. not a byproduct of my current job) in which I employ one
of such methods to solve a potentially real problem would be enough to
be relevant in my cv?

Contributing to open-source projects will link you to other engineers in the same field, and this will eventually open you many paths.
Also, try the book: Artificial Intelligence: A Modern Approach Stuart Russel, Peter Norvig, Prentice Hall, 3rd edition, 2009