Machine learning and artificial intelligence are becoming increasingly important topics in today's rapidly evolving world. With the help of these technologies, businesses are able to make more informed decisions, automate tasks, and even develop new products. For example, the introduction of ChatGPT (by OpenAI) to the public is going to have a big impact on future work. More models and use cases that will have massive adoption will come. It also leads to new tools which can detect AI-Generated text.
From beginners to experienced professionals, everyone can benefit from understanding the principles and limits of machine learning and AI. Want to learn more but don’t know where to start? There is a plethora of books out there to help get you started, but it can be difficult to know which ones are the best. In this blog post, we’ll look at some of the best books on machine learning and AI for beginners. From introductory to more advanced texts, we’ll cover a wide range of topics (computer vision, natural language processing – NLP, and data science…) to give you the best possible start to your journey into AI and ML.
First, If you want to start with AI and machine learning, we need to know two things before:
I would recommend learning to code in Python because it's a common language for developing data science and deep learning projects. Python machine learning is not only that, but with more experience, you are able to create web services and large-scale systems. Your first steps can be with Codeacademy or Coursera. They offer a lot of courses on computer science, algorithms, and programming. There are also books like Learn to Program with Python 3.
With math, it can be more tricky. Understanding vectors (and operations on them) and matrices is a must. Machine learning is an elegant (engineering) way to solve real-world problems via pattern recognition. The more advanced models you build, the more mathematical and conceptual background you will need. A Math specialization on Coursera can help you refresh some lost memories about linear algebra, calculus and statistics.
So let's take a look at which books can be very helpful at the beginning of your machine learning journey.
Life 3.0 by Max Tegmark is a really nice book about artificial intelligence. A very interesting start of the book is about a team that is developing AI technology. This AGI is able to create visual content, and eventually even games and movies. I really like the imaginative scenario that became a reality now with ChatGPT and DALL-E models. With this book, you will not learn how to code or create your AI, however, you will get an idea of how AI technology transforms our future.
If you are looking for similar books then there are a lot to pick from, for example, Superintelligence: Paths, Dangers, Strategies by Nick Bostrom is an important and timely book, packed with a lot of information about the benefits and dangers of AI. The last recommendation would be AI Super-Powers by Kai-Fu Lee about two US-China competitions in AI.
This one was one of my first introduction books to machine learning and smart algorithms. It will teach you fundamental concepts and algorithms that power the world wide web. The book explains classic algorithms inspired by nature and collective intelligence (like neural networks, collaborative filtering, swarm optimization, genetic algorithms, …), which will eventually help you later on your machine-learning journey. Programming Collective Intelligence is definitely not a book about the latest advanced concepts of neural networks. A lot of the problems are now solved differently. But it's a great start for total beginners, with a lot of practical examples written in Python language. After reading this, you will be able to build a Netflix-like recommender system and much more.
The Hundred-Page Machine Learning Book provides a simple and short overview of machine learning techniques and nice explanations of basic concepts for absolute beginners. In hundred-twenty pages, you will learn what is the difference between supervised and unsupervised learning, regression algorithms, support vector machines, feature engineering, decision trees, optimization algorithms (gradient descent) and clustering. With these concepts, you will be able to easily move to deep learning (neural networks) and start reading simple AI research papers, and blog posts and move to books discussed below.
All three following books are great if you already know how to code and know the basics. You will not make a mistake picking one. Luckily, modern machine learning frameworks such as Scikit-Learn, PyTorch or TensorFlow make it really easy to train advanced & powerful models (deep neural networks).
I would definitely recommend this one with a combination of Fastai online courses. You will learn every modern technique of machine learning, touching both worlds of computer vision and natural language processing. Many people find great jobs in the AI field, because of Jeremy Howard. This is a practical guide, showing innovative engineering approaches to problems. You definitely don't need a PhD to create amazing AI applications.
For people interested more in TensorFlow Framework instead of PyTorch, I would recommend this AI book by Aurelion Geron (ex-Google/YouTube researcher). The book is divided into two parts. The first part of the book is focused on standard machine learning algorithms. In the second part, the book discusses neural networks and deep learning, where TensorFlow is more useful than Scikit-Learn. A lot of topics are explained specifically for the TensorFlow framework.
Dr. Sebastian Raschka with Yuxi Liu and Dr. Vahid Mirjalili do a great job explaining advanced things in practical codes that are accessible in the GitHub repository. From building training datasets to implementing generative models that can create stunning images. Even exotic topics such as graphs or reinforcement learning for playing computer games are discussed. The book has more than 700 pages and it is worth every penny. If you get a deep understanding of these algorithms & concepts, then you can already consider yourself a senior AI engineer.
This is a bit more advanced book, definitely not for total beginners. The people behind it are some of the best researchers in the world. It is a theoretical book with a lot of equations and beautiful figures. Definitely great if you want to push your math understanding behind machine learning models. If you prefer hardcover, then it is available in online stores.
I would definitely recommend trying to develop your own project. There are several options, reimplement some architecture or algorithm yourself to gain a deeper understanding. Integrate it into some API and you have a smart system that other people can use. If you publish it on GitHub, you will have something to show on job interviews. Of course, there are also options for those who don't want to do the coding part, such as no-code machine learning platforms like Ximilar.
Another option is to compete or pick some projects from the Kaggle.com platform. Kaggle is an AI community platform with data science competitions (some by companies with interesting price money). There are many real-world examples and problems to solve. You will learn a lot by putting learned machine-learning concepts into practice. Some amazing solutions are publicly released by other Kagglers, so it is a great learning source. If you are more interested in Kaggle, there is also a book called The Kaggle Book: Data analysis and machine learning for competitive data science. It also contains great interviews with successful Kaggle data scientists.
After reading, practicing and getting fundamental concepts, you will be able to specialize yourself. You can, of course, specialize in some fields like natural language processing (NLP) and then specialize even further, for example, in machine translation. NLP and computer vision are the most researched areas, however, more fields are adopting AI for working with data. There are a lot of fields that you can be a specialist in. Here are some of the specializations and books with courses that you can start with:
There are a lot of other fields that AI is part of, like robotics, speech recognition, bioinformatics, physics, simulations, networks, big data, graphs, edge devices and IoT, deployment, the architecture of AI systems, optimization, … but they are out of the scope of this article.
The combination of books and online courses (like videos and MOOC courses) can be a very powerful learning tool. There are a lot of learning resources to pick from. Here we proposed several books for absolute beginners to advanced practitioners. I recommend starting with shorter and easier pieces like The Hundred-Page Machine Learning Book and then picking one with a lot of practical examples such as Machine Learning with PyTorch and Scikit-Learn. After that try to build something on your own or solve some Kaggle challenges.
Michal is a co-founder of Ximilar and a machine learning expert focusing mainly on image recognition, visual search, and computer vision. He is interested in science and loves reading books and choking people at Brazillian Jiu-Jitsu training.
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