Raw LLM Responses

In a nutshell: Step 0: Understand that machine learning is really just mathematics. Programming is just the way to apply it and is the easy part by far. If you are one of those people who "just hate math" then you are very unlikely to understand machine learning. NOTE: almost nobody is incapable of understanding math. You can likely do so even if you think you can't. Step 1: Get a good understanding of Calculus, Linear Algebra and Statistics. This is the the most important and most time consuming step. Luckily you are likely already on a path to its completion or have completed it already. If so, proceed to step 2. Step 2: Learn a good programming language for machine learning. Now it's tricky to say which ones are good and which ones less so, but a good start is to just go with Python. I want to stress that programming is the much less important and easier part of machine learning compared to mathematics. Note: just because it's a fairly widespread misconception by people who don't actually have any idea what machine learning is, such as the person who responded to you with the awful advice to just learn python, this is MUCH less important than Step 1. I repeat, get Step 1 down before even looking at Step 2. Step 3: Pick up a book that teaches machine learning. Do NOT just read the book. As you learn each new concept, create projects that utilize it. If you just read, you might think you are getting somewhere, but you are not. Derive equations such as those of backpropagation from neural networks yourself (I don't mean reinvent the wheel, but after looking at how the author does it, repeat without looking) Step 4: Create a larger project that uses a lot of things you learned. Try to do this as independently as possible. Step 5: Keep learning. When you think you are done, return to step 5. P.S. Another way is to just use a tool that does all the heavy lifting for you. You can certainly go that route, but I'm not interested in explaining that because I find it far less exciting.