I did a similar transition as a freelance, it was not a simple task. I started 7 years ago.<p>First, I got lucky to be doing data close to where a business case allowed it. Then had to fight my client so that I could _improve_ his best selling marketing campaign. I had to resort to implement explainable ML to convince him the back box was taking the same decisions, and only then adding long tail signals. At the time SHAP did not exist, but its precursor did (interpretableTree by Saabas, I ported it from Python to Scala/Spark because Python scared the client)<p>This was one-shot. Back to data integration after that.<p>I participated in an ML challenge, nights and weekends. And developed ideas from there, and personal techniques that I showcased here: <a href="http://explicable.ai" rel="nofollow">http://explicable.ai</a> . Although I didn't make any money from it (I tried, but while people like nice pixels, they don't need them; and I interest mostly engineers, who won't/can't buy it but will definitely want to do the same for themselves)<p>But it did act as a great portfolio piece. And landed me my first full ML project. It was a very small fixed time contract and ended recently, but I had a lot of fun doing it.<p>Anyway, this doesn't garantee you the greenest grass. For example, I'm currently looking for a salaried position for personal reasons and it's still tough. Btw if anyone wants to hire an MLE in Paris or remote worldwide, here is my resume: <a href="https://benoit.paris/CV_Benoit_Paris_EN.pdf" rel="nofollow">https://benoit.paris/CV_Benoit_Paris_EN.pdf</a><p>Now for your question: MOOCs can help a lot, and it's definitely something to put on your CV. Lots of good resources on YouTube as well. Karpathy's stuff is awesome for intuition (see for example LSTMs <a href="https://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow">https://karpathy.github.io/2015/05/21/rnn-effectiveness/</a>). You may also want to widen your focus to breakthroughs in other domains, reading papers and code as they come (UMAP, Nerfs/GaussianSplats). <a href="https://paperswithcode.com/" rel="nofollow">https://paperswithcode.com/</a> is awesome for that.
Also be sure to continue doing projects you can show a prospective employer in parallel.<p>TL;DR: Keep at it, but make sure you produce something, even if it's just nice pixels.