I reviewed 20 AI engineering courses, here are my top 5

0:00 I reviewed 20 AI engineering courses and in this video I'm going to share with you my top five. Now we're going to go over all of the evaluation criteria. I'm going to share with you the price point, how interactive they are, which ones are best for beginners or advanced users. And by the end of this video you're

0:16 going to know which course I would personally recommend if I was trying to get into AI engineering in 2026. Now with that said, before we can get into the courses, we need to understand what AI engineering actually is. Now first of all, this isn't machine learning research. This is about

0:32 building production grade applications with pre-trained models. Now that involves LLMs, right? And calling various APIs like the OpenAI API, the Anthropic API, various different models, using AI agents, doing agent evaluation or model evaluation, prompt engineering, MCP servers, and all of those types of

0:51 tools. Now this is one of the highest leverage skills in software engineering right now and pretty much every single product is becoming AI powered. Now the main thing here is that you don't need a PhD, you don't need to be a master in calculus, and this is really about practically building apps and getting

1:08 something in front of users as fast as possible. This is the complete opposite of machine learning research or deep theoretical studies. You're not building models, you're taking all of that hard work that's already been done and then applying that to real world problems to actually deliver results very quickly

1:24 for companies. Now I spent the last week researching all of the top courses that cover exactly that. And while I was doing that I came up with some evaluation criteria that I want to share with you now and that I'll go over for the five courses I'm sharing in this video. Now first for every course I

1:39 looked at, the number one thing I was checking was was it practical? Now does it build real applications or is it just explaining theory? Next I looked at the credibility. So who made it? Have they shipped any AI products? What's the company behind it? Then I looked at the interactivity. This is super important

1:55 when you're learning so I wanted to see was it hands-on, was there exercises, or was it just passive videos? Then I looked at the depth. Did this just skim the surface, or did it actually go deep into production-level concepts and help you get your hands dirty? And then finally, I looked at the key audience.

2:11 So, was this for beginners, intermediates, or advanced? That doesn't necessarily make the course better, but it's important to understand because everyone's at a different point in their journey. So, with that said, I have the five courses here. Now, I wasn't able to really rank them one through five

2:24 because they are somewhat different, but these are the top five, and one of them will probably be the best for you, but it will likely be different for many of the people watching this video. So, let's start with the first course on my list, which is a bunch of short courses from Deep Learning AI. Now, these

2:39 different courses that you can see on my screen here are all free to use, and you just have to pay for a membership if you want to access the interactive components. Now, Deep Learning AI is built here by Andrew Ng. Andrew Ng, I'm not exactly sure how you say his name, and he's extremely credible. He worked

2:54 at OpenAI. He's built and shipped real production-grade AI applications, and they have all kinds of content here. And the thing that I really like is that it's built into short-form concepts. If we just click into one of these courses here, let's go AI Python for beginners, we can see it's about 10 hours long, or

3:09 at least that's their estimate. Tells you the level, in this case it's beginner, the number of video lessons, and the code examples. Now, these courses are great. The one thing I will say is that it can be a little bit overwhelming because there are so many on this platform, so it could be tricky

3:22 which one to know to go to, and it does take a little bit of time to find which course you actually want to go with. But again, it's free, highly credible instructor. It's not super interactive. You can get some interactive components, but you do need to pay for that, but generally speaking, I think this is a

3:38 good place to get started with and will give you a lot of different options and allow you to kind of be exposed to some AI concepts without making a massive investment. Now, because deeplearning.ai is a little bit all over the place with the courses that they have, I was actually able to find that they

3:52 partnered up with AWS on Coursera to create a course called Generative AI with large language models. This is three modules. I believe it has some of the same content that they had on their deep learning platform, but it's structured into various different weeks where it includes fine-tuning, GenAI,

4:08 reinforcement learning, and a lot of the topics that you need to know to become an AI engineer. And I think this one is a great one to go with if you already have a little bit of experience and you understand Python, for example, because it gives you a little bit more structure. So, I'm not including it as a

4:21 completely separate course on my list, but kind of just an add-on to the previous one that provides a bit more structure. Now, the next course on my list is the Associate AI Engineer for Developers Track from DataCamp. Now, this is one of the best courses for getting into AI engineering, especially if you already

4:36 have a little bit of a background and you know some basic Python. Now, if we scroll down to the curriculum here, you can see it covers working with the OpenAI API, prompt engineering, working with Hugging Face, LLM Ops, which is a super important concept a lot of courses seem to miss, embeddings, topic

4:51 analysis. We have all different kinds of projects. And DataCamp is one of the most interactive platforms out there because pretty much everything you're doing is interactive and directly inside of their web UI. So, you don't have to download a Jupyter Notebook. You don't have to run the code on your own

5:05 computer. You can do it directly from their platform, which is why I've personally been recommending DataCamp for years now. And all of the courses, while they're not built by creators or YouTubers like me, have a really solid curriculum and are extremely well-rated, incredible. For example, you can see

5:20 people from Bank of America, Pfizer, Uber, etc. have all used these types of courses, and that's why I rank it very highly, especially for becoming an AI engineer. Now, for this particular track, again, you definitely would already want to understand Python and have some fundamentals down. But if

5:35 you're someone who's already developer looking to get into the field, you already have the basics, you've had a little bit of a taste, then I think this is a really good place to go and it's going to give you that depth without focusing too much on the theory. So, if we go through the rankings here, it's

5:48 very highly practical covering a lot of the main topics that I discussed kind of in the intro here and that you want to understand. It's very interactive because of how the platform is set up where you're actually doing exercises, working on projects, and not just watching videos. It goes into enough

6:02 depth without getting too heavy into the theory. And again, like I said, more for kind of that working developer type audience. Now, the next course on my list here is the LLM course from Hugging Face. This is a fantastic free course that has a ton of content. You can see it covers transformers, fine-tuning,

6:18 sharing models, different data sets, natural language processing, and has a bunch of different modules here completely for free. I'll link this one in the description. Now, it definitely goes extremely in-depth on a lot of the topics that you see here and much more in-depth than other resources you'd

6:33 find, especially for free. However, there are a few places that it lacks. For example, it does have some of the quizzes as you can see here and a little bit of interactivity, but it's not going to be the same level where you're actually writing code in your browser. A lot of this is going to be you copying

6:46 and pasting and kind of just reading the material. So, if you're someone who prefers videos, if you like really in-depth kind of interactive exercises, this probably isn't the best one for you. And it's not going to be as practical as some of the other options because it really focuses much more on

7:00 just the Hugging Face ecosystem as opposed to some of the other tools that are out there that you would want to know for AI engineering. That said, it is a fantastic resource. It goes very, very in-depth. It has S-tier credibility. And this is really meant for people who are going to want to be

7:14 working with open-source models, self-hosting models, who want to do fine-tuning, understand what's going on behind the scenes, and get a little bit more into the theory than all of the kind of practical AI engineering that a lot of the other courses cover. Now, the next course on my list here is the Full

7:30 Stack LLM Bootcamp which comes from fullstackdeeplearning.com in partnership with UC Berkeley and actually was filmed or I guess produced by UC Berkeley alum. Now, this is one of the most practical courses on this list. It covers all of the main concepts you need to know about AI engineering like

7:47 prompt engineering, LLM Ops, augmented language models, LLM foundations. It goes deep enough into the theory without being super heavy, but really covers the core kind of topics that you would want to understand. Now, that said, this is purely video lectures, so it isn't very interactive and it might be hard to

8:04 retain the information, but you're getting some of the highest quality education from some of the top people in the field completely for free, which I think can't go understated. Now, this is definitely going to skew to a bit more of an advanced audience, those of you who have already worked with machine

8:18 learning models, built some basic AI apps, and are really at the point where you want to deploy them and kind of level up and learn some stuff that's a bit more complex. And you can see that just from the fact that they have UX for language user interfaces, right? LLM Ops, prompt engineering, LLM

8:33 foundations, launch an LLM in app in 1 hour. You can see the architecture diagram there they have the thumbnail. And this is simply just eight lectures, not going to be, you know, super long and cover everything, but I think it's definitely worth checking out. It's also worth noting that the recordings are

8:47 from 2023 here. I don't think that undervalues them, but that just means they don't have some of the newer concepts like AI agents, which are a lot more popular, MCP servers, and some of the newer developments that have happened especially in open source local models, etc., as we push here into 2026.

9:03 Regardless, great content, have a look at it. Now, let's move to my final course recommendation. Now, the last course on my list here is the Associate AI Engineer for Data Scientist track from DataCamp. Now, this is the second track that I have on my list from DataCamp because I really do like the

9:18 platform and because of the interactivity that they have. Now, if I scroll through here, you can see that this covers training and fine-tuning the latest AI models for production, including LLMs like Llama 3. And if we scroll down here, you can see that we have supervised learning with

9:31 scikit-learn, unsupervised learning in Python, working with Hugging Face, um what is it? Intermediate deep learning with PyTorch, a bunch of stuff here. And this really covers some more depth in terms of the data science related field and doing things on kind of a lower level with LLMs, with AI models, as

9:50 opposed to the developers track that we looked at previously, which is a little bit more practical and focused much more on just purely AI engineering. So, this is going to skew more towards those of you who are data scientists or analysts who are moving into AI engineering. And if you already know things like Pandas,

10:05 SK learn, then this is definitely going to be the path for you. But if you're a pure back-end guy, kind of more like myself, you're probably going to want to look more at the developers track instead. And that's going to feel probably a little bit more tangible. Now, like I talked about DataCamp

10:17 before, they have a very interactive platform. Pretty much every single lesson has a full interactive, kind of terminal-based or web-based, you know, editor where you're writing code, you're doing exercises directly in the browser, and then you're answering questions, building projects, and everything is

10:33 designed to help you keep that retention high. Because when you're just watching videos, it's very difficult to retain the knowledge compared to if you actually have your hands on the keyboard, you're typing away, and you're learning alongside the instructor. Like I said, DataCamp is highly credible.

10:47 They have a ton of great reviews, and I've personally used the platform a ton myself. And while you do need to pay to access this course, I think that the structure and the exercises you get alongside it are well worth it. And one of the best parts here is that because DataCamp is a long-term partner of my

11:00 channel and they're sponsoring today's video, they're letting me give away a 25% discount to my audience. And yes, I know that all of you guys are going to say, "Okay, yes, of course he's recommending DataCamp because they're the sponsor." But what I want to say is that I've been recommending DataCamp for

11:13 years now. You can go back to videos, you know, two and three years old before they ever were a sponsor of this channel. It's a platform I've personally used myself that I know many people have gone through, and that I do truly sit behind it. That's why I'm happy to have them as a long-term sponsor. I've been

11:27 working with them for a very long time because I like the product and I like the platform. So, if you are someone who is serious about learning AI, you want to become an AI engineer, then this is a great hands-on learning experience and you can check it out. If you want something that's a little bit less

11:41 committal, you don't want all of the interactivity and you want maybe just pure videos or pure text, there's a lot of other great options on this list that you can definitely check out. Now, with that said, we went through five different courses here, which are all a lot different. So, what I want to do is

11:54 do a quick summary and give a general recommendation on which one you should pick based on your current situation. So, if you're a total beginner and you want everything for free, then go with the Deep Learning AI Short Courses and then look at the Full Stack LLM Bootcamp. Now, if you want

12:09 well-structured courses that are hands-on, interactive, and also provide a certificate, then check out DataCamp. Like I mentioned, you can go with the Developer Track or the Data Scientist Track, depending on which one you fall into. Now, if you want a lot of depth and you're focused on open-source

12:22 models, definitely go with Hugging Face. And if you want to understand the whole life cycle, go with the Generative AI with LLMs course. And then lastly, if you're already shipping AI applications and you just want to level up and get a bit more complex and in-depth, then again, check out the Full Stack LLM

12:38 Bootcamp. Overall, I think all of these are worth having a look at, but that's a quick recommendation at least for which one I would start with based on the situation that you're in. So, in that case, guys, I'm going to wrap up the video here. If you enjoyed, make sure to leave a like, subscribe, and I will see

12:50 you in the next one.