I recently did my finished a ML course at my university and I've been also reading up a lot on the subject. However, I need to materialize what I've learned into some practical work, i.e., projects. My ultimate goal would be to become a ML engineer, but I feel like even to get an internship in the area I'd already need to have some projects to show.<p>What do you recommend? Project-oriented books/tutorials?<p>Thanks!
One of our guys started a tutorial series on writing an AI to play the game, DOOM. It assumes very little knowledge and is a fun exercise to do when taking a break from some of the great courses posted here. It's no replacement for the excellent course work, but it is fun.: <a href="https://www.codelitt.com/blog/doom-ai/" rel="nofollow">https://www.codelitt.com/blog/doom-ai/</a><p>My favourite course for grasping the foundations of the concepts was Andrew NG's course (although it seems like you're beyond this now): <a href="https://www.coursera.org/learn/machine-learning" rel="nofollow">https://www.coursera.org/learn/machine-learning</a><p>I think the best way to learn, is to build things though. Have you checked out the Kaggle challenges? <a href="https://www.kaggle.com/" rel="nofollow">https://www.kaggle.com/</a> Those will give you great practical skills.
Have a look at: <a href="http://www.cs.cmu.edu/~10701/projects.html" rel="nofollow">http://www.cs.cmu.edu/~10701/projects.html</a> for project ideas. Project A1, A2, B etc listed on the page. You can then apply the toolkit you used in the ML course and solve these and make a portfolio of your ML projects.<p>Kaggle could be another option.<p>Else a well defined problem that you may want to solve in a domain: example graphics: See some papers here: <a href="http://www.cs.ubc.ca/~van/research/machlearn.html" rel="nofollow">http://www.cs.ubc.ca/~van/research/machlearn.html</a> which you could again solve in software.