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 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 building production grade applications with pre-trained models.

Now that involves LMS, 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 tools.

Now, this is one of the highest leverage skills in software engineering right now

in 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 a bill practically building apps

and getting 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 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 then I'll go over for the five courses I'm sharing in this video.

Now, first, for every course I 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?

Then I looked at the interactivity.

This is super important 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.

So was this for beginners intermediate 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 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 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 component.

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 works 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 ten hours long, or 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.

And 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 can be tricky 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 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 deep learning AI is a little bit all over the place with the courses that they have.

I was actually able to find that they 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 gen AI,

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 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 data.

Now this is one of the best courses for getting into AI engineering, especially

if you already 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 LM ops, which is a super important concept.

A lot of courses seem to miss embeddings, topic 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 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 and credible.

For example, you can see people from Bank of America, Pfizer, Uber, etc.

all use 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 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 that I think this is a really good place to go

and is going to give you that depth, the focusing too much on this theory.

So if we go through the rankings here, it's 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 depth though, 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 Huggingface.

And this is a fantastic free course that has a ton of content.

You can see it covers transformers, fine tuning,

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 we 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 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 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 is.

It really focuses much more on 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 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.

Then all of the kind of practical AI engineering that a lot of the other courses cover.

Then the next course on my list here is the full Stack Lamb Bootcamp,

which comes from full stack deep learning.com in partnership with UC Berkeley.

And actually it was filmed or I guess produced by UC Berkeley alumni.

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 Prompt engineering,

LM ops, augmented language models, and then 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 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.

This is definitely going to skew to a bit more of an advanced audience.

Those of you who have already worked

with machine learning models, build 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?

LLMOps prompt engineering, LLM foundations, launch an LM, an app in one hour.

You can see the architecture diagram there. They have the thumbnail.

And this is simply just a lecture.

So it's 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 from 2023 here.

I don't think that undervalues them, but that just means they don't have some of the newer concepts.

Like I Asians, 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 pushed here into 2026. Regardless, great content.

Have a look at it.

Now let's move to my final course recommendation then the last course on my list.

Here is the associate AI engineer for Data Scientist track from Data Camp.

And this is the second track that I have on my list from Data Camp, because I really do

like the 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 LMS like Lama three.

And if we scroll down here, you can see that we have supervised learning with scikit learn,

unsupervised learning in Python, working with hugging face.

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 opposed to the developer 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 sklearn, then this is definitely going to be the path for you.

But if you're a pure backend 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 data Camp before, they have a very interactive platform.

Pretty much every single lesson has a full interactive

kind of terminal based or web based 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 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, Data Camp is highly credible.

They have a ton of great reviews, and I personally use 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 Data Camp

is a long term partner of my 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 Data Camp because they're the sponsor.

But what I want to say is that I've been recommending Data Camp for years now.

You can go back to videos. No.

Two and three years old before they ever were a sponsor of this channel.

It's 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, and I've been 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 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 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 Learning Bootcamp.

If you want 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.

If you want a lot of depth and you're focused on open source models, definitely go with hugging face.

And if you want to understand the whole lifecycle, go with the generative AI with LMS course.

And then lastly, if you're already shipping AI applications and you just want to level up

and get a bit more complexity in depth, then again, check out the full stack LMS 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 leave a like subscribe and I will see you in the next one.