If you Want to Become a Data Scientist or ML Practitioner - [Learning Path]

cristi (70)in #machine-learning • 6 years ago

Tirthajyoti Sarkar, a semiconductor professional who is applying machine learning in his job, has written a very lengthy post in which he documents his learning path for machine learning. His advice is targeted to all professionals in non computer science fields who want to learn and contribute to machine learning and also data science.

He was very foreign to field, as he says:

"I could not tell a linked list from a heap. Support vector machines sounded like (few months back) some special equipment for people with disabilities." [source]

Reason being, he dove deep into taking online courses. Many of you know that nowadays you can take a course on any subject matter you'd prefer and it's very likely you can do it for free. Go-to MOOC sites are Coursera, EDX, Udacity, MITOpenCourseware, and others. But you should first consider whether or not it's worth your time investment and if it will be useful to you at some point in time.

Tirthajyoti started from the very basics, on Codeacademy, learning Python. In his own words:

"In all likelihood, you cannot go more basic than this :-). It worked though. I had this aversion towards coding but the simple and fun interface and the right pace of Codeacademy’s free course was appropriate to excite me enough to keep going." [source]

Then he went on taking a Udemy course (paid), a couple of courses from a professional program on EDX, and a few more.

Math is central to machine learning, which is why Tirthajyoti recommends doing/learning as much of it as possible (calculus, linear algebra, stats are to name a few). He also recommends a few courses with respect to this.

And probably, the most important part of the post is where he recommends resources very specific to machine learning. But for that, I'm going to let you read the rest of the post yourself:

If you Want to Become a Data Scientist or ML Practitioner - [Learning Path]