I’ve been working as a full-stack developer and recently transitioned to a role as an AI developer. While I’m excited about this new journey, I’m a bit unsure about where to begin my learning in AI to establish a strong foundation.
Could experienced folks recommend some good beginner-friendly courses or resources for getting started in AI? Should I focus on specific topics like machine learning, deep learning, or AI tools/libraries first? Any advice on structuring my learning path or prioritizing key areas would be greatly appreciated.
@Raghunath well, you certainly have come to the right place ! Seeing as you have some programming experience (presuming Python, otherwise you will have to pick that up first), most people start with MLS (the Machine Learning Specialization), and then quickly proceed to DLS (the Deep Learning Specialization).
Note these courses are primarily ‘low-level’ (i.e. theory / practice focused).
If you prefer an approach that focuses more on utilizing libraries you might check out the excellent offerings at Jeremy Howard’s Fast.ai.
Finally, if you have a strong interest in ‘ground up’ LLM’s, Karpathy’s Zero to Hero Course is currently the best on offer: Neural Networks: Zero To Hero
If you have some python programming skills, then the Machine Learning Specialization will explore some ML methods and introduce some tools and packages.
I hope you’re doing well! It’s great to hear about your transition into AI. Given your background in full-stack development, you’ve got a strong foundation to build on. If you’re comfortable with university-level math like probability, statistics, and linear algebra, diving right into a Machine Learning course could be a good start. Otherwise, starting with the Mathematics for Machine Learning specialization might give you the grounding you need.
Based on what you enjoy, there are a couple of paths you could explore further. If you like working with text or want to analyze sentiments, taking up Natural Language Processing and then moving to practical TensorFlow applications could be very engaging. If images and objects catch your interest more, starting with TensorFlow for image analysis and then perhaps exploring AI applications in medicine might be right up your alley. For those intrigued by image creation, the GANs course offers a creative take on generative AI.
After wrapping up these courses, I find that tackling a personal project, like something from Kaggle, helps solidify all that learning. It’s a great way to apply your skills in real-world scenarios and see tangible outcomes from your efforts.
On a related note, I’m currently working on an academic prototype for DeepLearning.AI to visually present AI learning roadmaps, aiming to make course navigation more intuitive. If you’re interested in seeing the paths laid out graphically and would like to provide feedback on this prototype, I’d be thrilled to share the Figma link with you. Your insights would be really valuable in refining the design and functionality.