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I studied machine learning when I was in university, a couple 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 ten 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 "introduction to machine learning", 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 of such methods to solve a potentially real problem would be enough to be relevant in my cv?

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    $\begingroup$ I'll say both two ways will work, just don't play with introductory kaggle matches, try to pick up a specific field you wanna develop in, perhaps CV, NLP, or recommenders, ML the concept is too broad, I don't see any position will let you apply all ML techniques, good luck. $\endgroup$ – leo lee Jan 10 '18 at 6:44
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Since your a software engineer,that sounds great ,so the programming language matters a lot. Study the algorithms and the logic behind the AI concepts for instance; when it comes to machine learning,there are open source projects that you can contribute to via git hub,and this really boosts your skills and experience.

I myself,am contributing to an open source conversation agents 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?

Remember this,contributing to open source projects,will link you to other engineers in the same field and help you a lot to study out.Given that you have a strong background in mathematics.

Also, try the book: Artificial Intelligence: A Modern Approach Stuart Russel, Peter Norvig, Prentice Hall, 3rd edition, 2009

Save the civilisation⊙

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    $\begingroup$ Thanks for the guideline. It will take some time to find a project I feel I can contribute to, since I always used tools and rarely wrote something from scratch. But I will try. I have a book titled "Machine Learning: a probabilistic approach". Do you think it is less of importance to me given my coder-oriented approach rather than the one by Russel and Norvig? $\endgroup$ – Lord Yalet Jan 12 '18 at 19:07
  • $\begingroup$ @LordYalet Human,as long as you have focus on what you want,read and be stable,so as you save the civilisation.Read the two books and try to see the difference between the two. $\endgroup$ – quintumnia Jan 13 '18 at 17:05

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