I'm trying to break into AI and machine learning, but I'm feeling a bit overwhelmed. I started a HarvardX course, but it was too advanced, and I couldn't keep up with it. I know Python and can build projects, and I understand some basics like search concepts, but I struggle when it comes to applying what I learn, especially with models and training. I'm in search of beginner-friendly courses or resources that explain things step-by-step rather than diving deep into theory. Also, should I prioritize learning the theory before starting to build small AI projects?
5 Answers
It seems like you might be struggling more with understanding the project lifecycle than the coding itself. Projects, especially complex ones, require planning on what classes or methods you'll need, rather than just jumping in. I’d say working on projects is usually more effective than taking courses.
I totally feel you! The pace of tech advancement is dizzying. I've been looking at the Bytebyteai course, but it's pretty pricey. Right now, I'm enrolled in a foundation course at maestro.org, which feels a bit repetitive since it starts from scratch, but I think it's essential to nail down the basics.
Starting with small projects is way more helpful than diving into theory-only resources. Practical learning tends to stick better, so I’d suggest picking a simple AI project to work on alongside your learning.
Learning AI and ML can be pretty daunting. It does require some advanced math, so don't be surprised if it takes a while. You might check out resources like the Machine Learning subreddit or various learning roadmaps available online to help guide your studies.
I'd recommend checking out Hugging Face. They have some beginner materials and projects that can really give you a practical sense of AI without diving too much into the heavy math right away.
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