Over the past four years with AI and machine learning, I would like to share all the resources that have helped me on my journey.
There are quite a few, so I'll break them down into the following categories:
- Programming and Software Engineering
- Mathematics and statistics
- Machine Learning
- Deep Learning and LLMS
- AI Engineering
Programming and Software Engineering
If you want to work in AI, you need to learn the program and have excellent software engineering skills.
The field is relatively new, so the de facto language of AI is still in the air. However, Python is the best way to learn for ease of use and AI infrastructure.
AI jobs were primarily spin-ups from machine learning. There, Lingua Franca is in Python, and this won't change anytime soon.
However, the most popular AI role, AI engineers, are closer to software engineering than machine learning engineering, so you may need to learn the following back-end languages: Java, go or rust.
I recommend starting with Python. It's much easier and I recommend you to make sure you understand the basics of major software engineering, but you may need to pivot the language in the future.
There are many courses and books, but the best teachers are consistent practice. Resources can help you get started on your journey, but creation and construction is how you really learn Python, and in fact any language.
My main recommendations for Python and software engineering fundamentals are:
- Learning Python – Full Course for Beginners– The first course was filming Python at the beginning of my journey. It's only 4 hours long so you can do it in half a day.
- Python specialization for everyone– This is Perhaps the most recommended course is for good reason. If you are after an end-to-end course to learn Python, this is it. However, the reputable “Intro to Python” course is sufficient.
- Hacker rank & Leetcode–I used this when preparing for a Python coding interview.
- neetcode– Use this resource to learn about data structures, algorithms, and system design. This is an excellent platform for learning all basic and advanced topics with hands-on exercises and provides great interview preparation.
- Harvard CS50 Introduction to Computer Science–No matter where you are in the online technology field, you've probably heard of this course. Probably the best intro to your computer science and software engineering course! A complete beginner, and actually highly recommended for anyone.
Mathematics and statistics
You might argue that you don't need to know mathematics as most AI jobs are primarily about implementing basic models, but if you want to be a top AI practitioner, you at least need to know how these models work under the hood.
The following resources are all you need to learn the mathematics you need. I don't think you need to look elsewhere.
- Practical statistics in data science (Affiliate link) –This would be true if you could only get one book to learn statistics. The main draw is to provide AI/ML practitioners with statistical knowledge and provide practical examples to Python.
- Mathematics for Machine Learning (Affiliate link) –It is a comprehensive book on the mathematics behind machine learning and AI, covering topics such as Calculus and Linear Algebra. It's going quite a bit, so I don't recommend doing everything end-to-end. Instead, we use it to learn as important concepts and reference text.
- Mathematics for the specialization of machine learning and data science – This is a newly released course by Deeplearning.ai, a well-known manufacturer of machine learning and deep learning specialties. It covers all basic mathematical topics, including calculations, linear algebra, statistics, probability, and more, which are ideal for beginners and are particularly relevant to AI and machine learning.
Machine Learning
Most of the current AI actually mentions genaia subsection of machine learning. As its name suggests, Genai is an algorithm that generates text, images, audio, and even code.
However, AI has been a concept for a long time, dating back to the 1950s. Neural networks have been born .
That's what Alan Turing saidTuring TestAfter working with computers and thought machines during World War II.
Anyway, my point is that AI is much broader than most people today think, and you need a solid basis for machine learning and traditional AI, and you need to be a great AI expert today.
The following list covers all baseline machine learning knowledge. If you want to learn more advanced topics Time Series Prediction, Reinforcement learning , optimizationor Computer Visionplease let me know, and I would recommend some to you.
- Practical ML using Scikit-Learn, Keras, and Tensorflow (Affiliate link) – If only one book was given to help with machine learning and AI learning, that would be it. It's great, covers almost everything you need to know, and at the end you'll get into LLMS, reinforcement learning, computer vision.
- Specializing in machine learning –The first course I worked on machine learning in 2020 was probably the best course in machine learning in history. When I took it, it was in the octave, but it was later revamped and is now in Python, with cutting-edge topics like recommended systems and reinforcement learning.
- 100 Pages ML Book (Affiliate link)– All machine learning is summarized on 100 pages! A truly great reference text to help you quickly search and get a review of things. It really covers the basics.
- Elements of Statistical Learning (Affiliate link)– Basics of machine learning, it is basically ideal for acquiring statistical learning. This book really teaches the essence of machine learning.
Deep Learning and LLMS
As shown in the above diagram, Deep learningA small category and machine learning subsection for the entire AI umbrella.
Deep learning is where all these generative AI algorithms come from. LLMS, diffusion, transformerAnd all other basic models work under the hood.
AI Engineering
At this point, we can get a thorough understanding of AI landscapes, especially the LLMS and Genai models, practically and theoretically.
Real value comes from creating products from AI models and knowledge. Therefore, we need to learn how to produce and deploy these algorithms so that they can benefit our customers and businesses.
Most AI jobs are so-called AI engineers, and are closer to traditional software engineering than machine learning engineer jobs.
It is mainly to use basic genai models such as Llamas, GPT-4and ClaudeBuild surrounding products. It is rare to do real model development, mainly because training for these models is expensive and the current basic models are very good!
- Practical mlops (Affiliate link)-This is probably the only book you need to understand machine learning and how to deploy AI models. I use it more as reference text, but I teach almost everything you need to know, including contentization, shell scripts, cloud systems, model monitoring, and more.
- AI Engineering (Affiliate link) –This book is very popular at this time. It is written by Chip Huenundoubtedly a major expert in ML/AI systems. She taught the course at Stanford! Therefore, you are in good hands with this book.
There are plenty of resources. The main point is that it's complicated and doesn't start. They all teach the same thing roughly, so you're not wrong, regardless of the course or book you're using.
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