Best Resources to Learn AI (For Developers)

A developer compiled a list of recommended resources for developers learning AI, including courses from fast.ai, DeepLearning.AI, Harvard's CS50 AI, and Google's Machine Learning Crash Course, as well as hands-on platforms like Kaggle, Google Colab, and Hugging Face. The guide also highlights tools for tracking experiments and exploring generative AI models.

Everyone is talking about AI. But if you're a developer who actually wants to understand it, not just use ChatGPT, but genuinely learn how the models work, how to build with them, and where the field is going, it can be hard to figure out where to start. The problem is that there are so many resources. Courses, YouTube videos, roadmaps, newsletters, books, papers... it can quickly become overwhelming. So in this post, I've put together some of the resources I think are worth checking out if you're a developer learning AI. I've included courses, hands-on platforms, YouTube channels, books, and communities. Let's jump right into it. If you're starting from scratch, I'd begin with a course. These can help you understand the basics before you start jumping between different AI tools, tutorials, and papers. If you want to learn deep learning by building things, give fast.ai a try. You don't need to start with a lot of complicated math. The course gets you building pretty quickly, and you learn the theory along the way. It's also free, which makes it a good place to start. If you prefer learning step by step, DeepLearning.AI is a good option. There are courses on machine learning, deep learning, generative AI, and a lot more. If you're completely new, start with the Machine Learning Specialisation and then move on from there. If you already know some Python and want to get into AI, then you can try Harvard's CS50 AI course. It covers things like search algorithms, machine learning, neural networks, and natural language processing. And you get to build projects while learning. It's free to access online. If you don't want to jump straight into a long course, start here. Google's Machine Learning Crash Course covers the basics of ML with examples and interactive exercises. It's a nice way to get familiar with the main concepts before moving on to something bigger. If you're more interested in LLMs and generative AI, you'll probably want to spend some time on Hugging Face. Their Learn section has courses and tutorials on transformers, LLMs, agents, computer vision, and other AI topics. It's especially useful once you want to move from learning about AI to actually building with it. Once you have the basics down, it's time to actually try things out. These platforms are useful for practising, running experiments, and getting your hands dirty with real AI projects. Kaggle is where you can actually practice. You get free courses, datasets, notebooks, and competitions to work with. If you're ready to start looking at AI research, Papers With Code is worth exploring. It connects research papers with implementations, benchmarks, and related code, making it easier to go from "I read about this model" to "I want to see how this actually works." If you don't have a powerful machine for running ML experiments, Google Colab can help with that. You can write and run Python code directly in your browser, and it also gives you access to GPUs depending on availability and usage limits. It's pretty handy when you're following an AI tutorial and don't want to spend time setting everything up locally. Once you start running more ML experiments, keeping track of everything can get messy. That's where Weights & Biases W&B comes in. You can use it to track experiments, compare runs, visualise metrics, and keep track of things like model parameters and results. It's not something I'd worry about on day one, but it's a useful tool to know about once you start building more serious ML projects. If you want to experiment with Google's generative AI models without setting up a full development environment, Google AI Studio is a useful place to start. You can try different prompts, test models, and get a feel for how generative AI works before adding it to your own projects. Sometimes you don't need another full course. You just want someone to explain a confusing topic in a simple way. These are some YouTube channels I'd recommend for that. If neural networks feel confusing, watch this series before jumping into a heavy course. 3Blue1Brown explains things visually, so concepts like gradient descent and backpropagation are much easier to understand. I especially recommend this if you're more comfortable with visual explanations than reading a bunch of formulas. If you want to understand what's actually happening inside neural networks, check out Andrej Karpathy's videos. His Neural Networks: Zero to Hero series builds neural networks from scratch and explains what's happening at each step. It gets more technical as you go, so I'd recommend starting from the beginning instead of jumping into random videos. If you prefer learning by looking at code, you'll probably like sentdex. There are lots of Python and machine learning tutorials where you can follow along and build things yourself. It's also useful if you don't want every AI tutorial to be 90% theory. Research papers can be pretty difficult to understand when you're new to AI. Yannic Kilcher's videos can make them a little easier to follow. He goes through AI research papers and explains what they're trying to do, how they work, and why they matter. You don't need to watch every video. Just pick papers that you're interested in. If machine learning concepts and statistics feel confusing, give StatQuest a try. Josh explains things like regression, decision trees, neural networks, gradient descent, and other ML concepts in a really simple way. It's a good channel to keep around when you come across a concept that just isn't clicking. If you prefer learning from books or want something you can come back to later, there are a few good ones worth keeping on your list. If you prefer books, this is one I'd keep on your list. It covers machine learning with plenty of code examples, so you're not just reading theory. You don't have to read the whole thing at once either. Pick a topic you're trying to understand and work through that chapter. This one is for when you want to go deeper. It's more technical and has quite a bit of math, so I wouldn't recommend starting here if you're completely new to AI. Learn the basics first and come back to this book later. If you don't want to read a 700-page ML textbook, this is a much shorter option. It gives you an overview of the main machine learning concepts without making you spend weeks on one book. It's a good one to keep around when you want to quickly look something up. The Hundred-Page Machine Learning Book https://themlbook.com/ If you're a developer who wants to build applications with AI, this is a useful one to have on your list. It focuses more on the practical side of building AI-powered applications, including working with foundation models and putting AI systems into production. If you want to understand how LLMs work by actually building one, then you should check out this book. This book walks through the process step by step, from the basics to implementing a GPT-style language model. It's more technical, so I'd save this one for after you've got the basics down. AI changes pretty quickly, so learning isn't really something you finish. These communities and newsletters can help you keep up with new research, tools, and what's happening in the AI world. If you're interested in open-source AI, you'll probably end up spending some time on Hugging Face. There's a huge ecosystem of models, datasets, demos, discussions, and learning resources. The community is also useful when you're stuck and want to see how other developers are solving similar problems. Reddit's r/MachineLearning is another place to follow discussions around machine learning research and new developments. It's especially useful if you want to see conversations from researchers, engineers, and people who are actively working in the field. The Batch is a weekly newsletter from DeepLearning.AI covering AI news, research, and other developments. If you don't want to spend your day checking AI news, this is an easy way to keep up. Another newsletter you can try is TLDR AI. It covers AI news, research, tools, and other updates in a short format. I like newsletters like this because you can quickly scan what's happening without opening 20 different tabs. If you want to follow AI research and understand what's happening beyond the latest AI product announcements, Import AI is worth following. It's a newsletter focused on AI research and developments, with commentary that helps put new papers and ideas into context. So, that's a lot of resources. You definitely don't need to go through all of them. AI is a huge field, and you don't need to learn everything to start building with it. If you're a web developer, maybe you want to understand how LLMs work, add AI features to your apps, work with AI APIs, or simply understand the tools you're already using. Start with what you actually want to build and learn the concepts you need along the way. That's much less overwhelming than trying to learn "all of AI." The important thing is to start building. You can always go deeper as you go. That’s all for today I hope this list gives you a good starting point for your AI learning journey. For paid collaboration, connect with me at: connect@shefali.dev mailto:connect@shefali.dev If you enjoy my work and want to support what I do buy me a coffee Every small gesture keeps me going 💛 Follow me on X Twitter https://x.com/Shefali J to get daily web development tips & insights. Enjoyed reading? You may also find these articles helpful.