after working in AI and machine learning for over four years I want to share some of the resources and books and courses that have really helped me in my journey As there are quite a few I'm going to break them down into the following categories Programming and software engineering maths and stats machine learning deep learning and LLMs and finally AI engineering Let's get into it If you want to work in AI you have to have good software engineering skills and also good programming skills This opinion is also backed up by Greg Brockman who's the current OpenAI CTO As the AI field is quite new the de facto language is still kind of up in the air However in my opinion your best bet is to learn Python Python is the one that's most commonly used nowadays when building any AI infrastructure project Majority of AI jobs have been spun up from traditional machine learning ones and in machine learning the lingua franker as you can say is Python and that's not changing anytime soon However I will say that the most popular current AI role is the AI engineer which is actually a lot more closer to software engineering than it is to machine learning engineering So it may well be worth learning a back-end language like Java Go or Rust I personally use Rust in my day job So we can see how it's not just Python but other languages may be used a lot in the future for most AI jobs But I still recommend that you start with Python because like I said a lot of the machine learning infrastructure and libraries are built around the Python ecosystem and I don't see that changing for at least half a decade now There are many courses books videos you know whatever to learn Python But by far the best teacher if you want to learn Python or any programming language or literally anything for that fact is practice So even though I'm going to give you some resources that you can use to learn Python don't worry too much about them and in fact everything in this video just use the resources I'm going to give you to just learn the fundamentals and then start implementing And that goes for anything in the machine learning AI or data science field Anyway I digress And my main recommendations for Python are the learn Python course by free code camp This is the first ever Python course I took It's 4 hours long and teach you all the basics I really recommend it Like I said it's what I got started with and it served me well so far The second one is the Python for everybody specialization This is probably the most well-known Python course at least on the Corsera platform and it's probably for good reason People seem to really like it I've actually personally never taken it but I hear such good things about it and like I said it's probably the most popular course out there probably for good reason I also used Hacker Rank and Leak Code just to get some hands-on experience on solving problems using Python And it's also very good for interview practice Another resource I use quite a lot is neat code I use this to learn data structure algorithms and also system design which are really fundamental topics you need to understand in software engineering if you want to land a job And finally another course that I've taken in kind of like drabs is the Harvard CS50 introduction to computer science It is literally like the best course out there if you literally know nothing about computer science It'll teach you all the fundamentals teach you some languages as well So I really recommend it if you're a complete beginner Even though many people will argue that you don't really need to know the underlying maths to become or work in AI because all the foundational models you don't really build models right you kind of just inference them or you import them in and you use them So you don't really need to know what's going on under the hood Now personally I don't really subscribe to that idea I think if you want to be a top AI practitioner then you should have some understanding behind how these LLMs and all these other generative models work under the hood And to understand how these models work under the hood you need to study the fundamental maths And in my opinion all you need is these following three resources The first one is a practical statistics for data science textbook I've recommended this book so much because it's probably the best book if you want to learn stats for data science maths or machine learning I should say and AI It literally covers everything and it's specifically applied for those fields and it gives you hands-on examples in Python So by far is the best book you can get if you want to learn statistics in those fields The second one is mathematics for machine learning Again this one is more on the linear algebra and calculus side So in general if you want to learn AI or machine learning they're kind of three areas you need to study Stats linear algebra and calculus The first textbook the one I recommended will study the stats And the second one mathematics for machine learning will give you the linear algebra and calculus side It's quite dense so I don't recommend reading the whole book but if you learn everything in that textbook then your math skills will be more than sufficient for a lifelong career in AI and machine learning And finally I recommend the course mathematics for machine learning and deep learning specialization This course is actually created from deep learning AI who created the machine learning and deep learning specializations which are by far the best courses on machine learning and deep learning out there So I've heard really good things about this course and if it's anything like those other ones they've created then it's by far in no way probably the best mathematics course you can take because it's also targeted towards those fields That's the main thing here We're not learning arbitrary maths learning maths that's directly targeted to AI and machine learning So we're learning all the relevant skills not just everything in the field because well I mean there's math degrees out there right so hey take those three resources They're by far and away the best ones and it'll cover literally all your bases And like I said it's only three of them So you get everything just using a handful full of courses Now let me give you a quick history lesson So what most people refer to as AI nowadays it's not actually AI is actually something called generative AI which is AI that generates images pictures videos etc Like chat GPT it generates text right however AI as a concept has been around for centuries and the current state of AI can actually be dated back to the 1950s when the first neural network was proposed It even predates that with Alan Cheuring coining the cheering test on the idea of computer science and thinking machines during the Second World War Anyway my point is that AI is so much broader than people may think it is And to be really proficient in AI you also have to learn machine learning to a really good level The following list I'm going to give you will cover all your fundamental knowledge you need in machine learning But if you want to learn more specialized skills like time series analysis convolutional neural networks reinforcement learning let me know and I'll give you some resources that I've used in the past and also have been recommended by other people So the first book I recommend is the hands-on ML with psych learn tensorflow and carers textbook I've recommended this book so many times If you could literally get only one book for your whole AI machine learning career it would be this one This teaches you pretty much everything All the fundamentals how to apply them how to code in Python like how to implement all these packages in Python And it even touches upon reinforcement learning LLMs and autoenccoders like all these complex things which again they're more of a not fundamental level But this book literally covers everything So by far and away if there's one book you would want to get from you want to buy watching this video or course watching this video this is the book You know it's linked in description below with like every other textbook but I highly recommend it You can probably find free versions online if you wanted to I just prefer having a physical copy But by far and away the hands-on ML with scikitle learn tensorflow and caras textbook is the best book on machine learning and AI you can get The second resource I recommend is the one that I took right at the beginning of my machine learning journey which is the machine learning specialization course It's taugh by Andrew and is by far and away like taught by the best one of the best AI ML researchers and it's probably one of the best courses out there It's probably one of the mo the oldest courses I think it originally came out in 2012 but it's phenomenal I really recommend it I took it and it's done wonders for my career I highly like I said I can't recommend it enough It's also been revamped and it's in Python now When I took it was back in Octave or Mat Lab So it's even more relevant because you actually be using Python You'll be using more upto-date packages and it teach you the theory and also the notebooks It's just amazing So again also really recommend this course Another one which is more for like bedside reading is the 100page machine learning book by Andre Bookov Like I said this one is more like a bedside reading in that it's only 100 pages It won't go into all the details and depth like a bigger textbook will do but it'll cover like the overall concept if that makes sense So it's really useful to have as a reference text or if you want to learn a new topic you can open it up find that a section and then you can research more about it online or however you want to do it But this book is really useful like I said to have like a reference book and also if you can look through that textbook and know everything in it then your knowledge is great And the final one is the elements of statistical learning This one is a bit more kind of traditional because it's more on statistical learning than machine learning but the two are very interlin This one is very dense and like I said it's more of a traditional book It's a bit drier but it goes into a lot of the theory really really deep So if you want to learn a topic to a really good understanding particularly if it's more of a traditional machine learning algorithm then this book is highly recommendable for you Now suppose you want a proper and thorough boot camp to learn machine learning In that case I recommend zero to masteries complete AI machine learning and data science boot camp who are kindly sponsoring this video It will teach you how to become a fullyfledged machine learning engineer this year and will cover topics like data analysis data science machine learning Python and pretty much everything else you need to secure a job in machine learning and AI The main reason I recommend this boot camp and course is their focus on building projects Like I said earlier the only real way to learn something is through consistent practice and building and getting hands-on experience This course will teach you to build applications and models like heart disease detection app a bulldozer price predictor and a dog breed image classifier and many many more There are also many other courses and career paths on their platform So I recommend checking out and seeing what you would like to take and what will help you on your journey But the best part is their community of over 500,000 students and instructors who will help answer any questions and help you prepare for a career in this field I've literally never seen any other platform have anything like this There's a reason that Zero to Mastery have gotten over a thousand students from zero to getting hired including top companies like Meta Google and Nvidia I will leave the AI machine learning and data science career path in the description below as well as the whole course catalog for you to check out Deep learning is where all these generative AI algorithms come from So you'll truly understand how things like LLM diffusion models and transformers work as well as all the other foundational models under the hood I will first begin by learning PyTorch because if you want to work in AI you should at least know one deep learning library Now in the field there's kind of two main libraries TensorFlow and PyTorch I personally recommend PyTorch because it's used more and by more research companies and more papers have written in PyTorch and it's kind of superseding as a de facto deep learning library over TensorFlow particularly in recent years PyTorch was used in about 77% of research papers published in 2021 and 92% of hugging face models are exclusive to PyTorch So like I said the general trend is in the direction of PyTorch So if you're choosing between PyTorch and TensorFlow I personally suggest you go with PyTorch Now after studying PyTorch I recommend you take the deep learning specialization This is the follow on from the machine learning specialization also taught by Andrew and it'll cover all the things like convolutional neural networks recurrent neural networks and even touch upon LLMs So it'll teach you all the deep learning stuff which is what you need if you want to understand how well deep learning and all these more sophisticated models really work After we've got the fundamentals in deep learning I'd then recommend taking the introduction to LLM's video by Andre Kapathy He's probably the leading research in AI at the moment and this 1hour video will basically give you a highle overview of where we currently are in the Gen AI particular LLM space and it'll set the scene for you and basically just make you understand more about the industry and where it's heading After watching that hour video I will then take Andre Kapathi's neural networks zero to her course This course will basically get you to build PyTorch or at least how PyTorch works under the hood from scratch So it's a really really good educational course It'll start quite simple with just getting you to basically make a neural network from scratch But at the end you're making a whole GPT from scratch So you go from literally zero to hero real quick all the way from neural networks to building GPT which is like the state-of-the-art in the moment from scratch No libraries literally just raw numpy arrays So it's really really good Again it kind of can be a bit technically hard but if you did a whole course and really understand what's going on then your foundational knowledge behind LLMs and diffusion models and all these sophisticated algorithms would be extraordinary And finally if you want a textbook then I recommend the hands-on language models textbook by Jay Alamar For those of you who don't know Jay Alamar is kind of probably wrote the most famous blog post on transformers It's called the illustrated transformer and it's probably the best explanation about transformers and what he did is basically took that blog post and made a whole book out of it obviously adding other things So this book is probably by the best guy who can explain transformers to you and he wrote a textbook So if you really want to understand things intuitively then this textbook I by far and way recommend and it's probably the only textbook at the moment that's like really up to date on LLMs because like I said it feels quite new But if you are looking for textbook then the hands-on large language models is the one I recommend you get So if you've taken all the courses and books I've recommended so far you have a really good understanding of the current AI landscape particularly when it comes to things like LLMs and Transformers and you have that theoretical but also hands-on knowledge as well So you're up to date with all the latest going ons and you understand what AI currently means in today's society Now the real value doesn't come from just understanding these systems It's being able to deploy these models and solutions to production so they generate business value customer value whatever it may be But the point is these models or these information you have in your head about these models need to go out into production and work for real life systems And also if you want to work in AI most AI jobs now are something called an AI engineer And an AI engineer is a lot closer to software engineering than machine learning engineering And what I mean by that is that if you're an AI engineer you're not necessarily building models from scratch because a lot of the best models like Llama Claude Chad are kind of already built and it's very hard to beat them because one you haven't got the computer resource Two the skills Three again the infrastructure to train these large language models You just can't do locally or by yourself So most of the AI engineer role is simply taking these existing foundational modules that we call them and implementing solutions products and building out the infrastructure around them to serve customers So you really need to understand how you can productionize these AI algorithms and that's where you need to learn AI engineering To learn AI engineering and how to productionize AI algorithms there are two books I recommend The first one is practical MLOps This one is around more how you productionize traditional machine learning algorithms but is very useful in getting you to understand their underlying theory like docker containerization cloud systems you know all the things you need to understand how to ship machine learning solutions because that's kind of like the backbone behind shipping AI solutions right so that's the first book I recommend and the second one is the AI engineering textbook now this one is raved about so much and I can see for good reason because the person who wrote it Chip Hun her name is she's kind of like the leading AIM ML deployment or basically she's a leading practitioner about how to deploy AI and ML systems So this book written you know by her is probably the best book you would get out there on AI engineering and it's literally called AI engineering So these are two books I recommend practical MLOps and the AI engineering textbook that'll cover all your deployment needs and we'll also teach you how to do hands-on examples with deploying both machine learning models and AI models too So we went through a lot of resources in this video and it may seem quite intimidating to begin with but don't worry too much I mean these resources are ones I've used over my journey and I've been studying this field for over 4 years and even so I haven't gone through every single textbook end to end The point is don't over complicate it If you want to learn something just pick one resources and start with it But you certainly don't have to go through everything end to end like read every word on the textbook Just learn the things that most relevant to you and then apply them and that's how you learn like I said at the beginning So I wish you luck in your AI journey and I'll leave you by this tweet by Andre Kapathy which perfectly summarizes how to learn and study AI how to become expert at anything One iteratively take on concrete projects and accomplish them depthwise Learning on demand Don't learn bottom up breathwise Two teach summarize everything you learn in your own words Three only compare yourself to younger you never to others I think that last point is the most important thing you can take from this video and just go forth and happy learning Oh and one more thing If you're after for some personalized coaching or like tailored advice then I offer one-to-one coaching packages CV reviews road maps basically anything that can help you get closer to your data science or machine learning journey I'll leave link in description below about all my services So check that out if you're interested in speeding up the process or if you want some more like I said tailored advice about your situation I'm sure I can help you