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How I’d learn ML in 2024 (If I Could Start Over)
I am a PhD candidate and the head of ML department in an automotive industry company, but it took me over 4 years to get to this point.
So, today, I will share how I would learn ML if I could start over by revealing the 6 key steps you need to take.
Let‘s get going!
Python
Typically, there’s no strict sequence for tackling these steps, but I’d advise against jumping straight to the final and most crucial phase first.
Instead, I highly suggest beginning with the fundamentals of Python.
Python is the go-to programming language for virtually everyone in the machine learning field, and it serves as the foundation for every subsequent step mentioned here.
This is particularly relevant for novices unfamiliar with concepts like lists or dictionaries, or those who haven’t yet mastered basic programming constructs like if-else statements or for loops. It’s essential, in my opinion, to also grasp concepts such as list comprehensions and class inheritance.
Frankly, if you’re unsure where to start, simply search for a “Python tutorial” or course on YouTube or Google and dive in. The wealth of exceptional, free resources available is astounding, but remember, it’s crucial to practice coding alongside the tutorials.
Maths
Jump into ML with Python to start things off on a fun note, but don’t get too bogged down in the details yet. Sure, the math part will come into play eventually, but that’s no reason to sweat it now.
You might think, “Hey, I don’t need math when I’ve got all these fancy Python libraries doing the heavy lifting!” And yeah, that’s partly true. But, to really get what’s going on in most ML stuff, you’ll need a handle on some basic math concepts like calculus, linear algebra, and probability.
Don’t worry, though — it’s not like you need to be a math whiz. We’re talking about the kind of math…
