Find the best Machine Learning Books!
Once you are comfortable with Python and with Data Analysis using its main libraries, it is time to enter the fantastic world of Machine Learning: Predictive models, applications, algorithms, and much more.
There are a lot of books out there that try to teach you Machine Learning; here we have only listed some of the best ones.
Enjoy them and welcome to the beautiful world of Artificial Intelligence, Deep Learning, Natural Language Processing and in general Machine Learning to the hand of these amazing books.
Before getting into more extensive coding ML books, we wanted to offer a book that is more related towards giving the readers an understanding of the main topics of Machine Learning and artificial intelligence in an elegant, clear, and concise manner.
Although there is code and maths in the book, the goal of the 100 Page Machine Learning book by Andriy Burkov is to provide a common ground for any kind of person with an STEM background to meet the wonderful world of Data Science.
It covers an amazing variety of topics but not in the depth that might be offered by other books (take into account it is only a little more than 100 pages), but it does so in a simple and clear manner, and it is useful for Machine Learning practitioners as well as for newcomers to the field.
In conclusion, a perfect aperitif to start learning about this wonderful topic. Read the full review here.
Hands-On Machine Learning with Scikit-Learn & Tensorflow is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.
After completing the whole book you should be ready to face a project by yourself and be confortable with the different steps in this process. You will be able to code most if not all of the machine learning algorithms in Python, and understand what you are doing through the whole process. Read the full review.
Python Machine Learning by Sebastian Raschka is one of the best books for learning how to implement Machine Learning algorithms. It does a great job introducing the theory and main concepts behind the most known Machine Learning algorithms, and the standard Data Science pipeline.
However, its main strength, and what makes the book a great companion in the learning career of any Machine Learning enthusiast, is the great practical implementations and detailed code explanations it includes. Find the full review here.
An Introduction to Statistical Learning provides the perfect introduction to the intersection between statistics and machine learning, covering topics that go from the most basic like linear regression to more advanced like Support Vector Machines and clustering techniques.
As the goal of the book, aside from teaching the main theoretical concepts and statistical foundations behind these techniques is to present them in a practical and applicable manner, each chapter contains a tutorial on implementing the analysis methods and prediction techniques in the R programming language. Read the review and find out if it is for you!
The Elements of Statistical Learning is the go-to book where many top academics will point when asked which is the best machine learning book about the theory, concepts, and workings of the algorithms and techniques.
It is neither a beginner nor a practical book: it is the text that will get you from implementing Machine Learning algorithms to becoming an expert on the guts of all the models and techniques. Find the full review here!
Machine Learning is a very beautiful theoretical field, and it’s powers and benefits are completely out of doubt. However, if Machine Learning models are not deployed to a productive environment and applications are built, these models serve no practical purpose.
The goal of Building Machine Learning Powered Applications is to explain in detail how to exploit these Machine Learning models to create beautiful, efficient, and useful applications and products that can provide real value.
If you want to go from theory to product, Building Machine Learning Powered Applications is one of the best available books for it. Find the full review here.
Introduction to Machine Learning with Python is a very practical book, oriented for readers who are comfortable programming in Python, and that want to learn Machine learning in a practical way, sliding away from heavy maths and complex theory.
It’s main focus is to teach programmers how to build Machine Learning applications using Scikit-learn, Pandas, Numpy and Matplotlib, in a way that is easy to follow and very hands-on, while briefly discussing the main concepts and terminology behind the Machine Learning algorithms it discusses. Find the full review here.
Deep Learning from scratch is the perfect book for those with Machine Learning, Python, and Math knowledge that want to get a profound knowledge fo the nitty gritty details of how Artificial Neural Networks work.
It will teach you so by explaining all the different concepts like the layers, back and forward propagation, metrics, and different elements step by step and with very good visual, code and mathematical explanations. This kind of learning will allow you to later build a knowledge of advanced topics with ease, and to face any problem that can be tackled with a neural network with confidence and clarity. Find the full review here.
Deep Learning by Goodfellow et al is the reference go to book in universities for teaching the theory behind Deep Learning. It is an exhaustively written book, with lots of theory and maths, oriented towards granting its readers a deep understanding of what happens in the guts of a Deep Artificial Neural Network. Because of this goal, the book contains very little code or programming references.
It is no surprise then, that this book was written by 3 of the top personalities in the world of Deep Learning: Ian Goodfellow, Yoshua Bengio (the Godfather of Deep Learning) and Aaron Courville. It is the go to book if you want to become an expert on Deep Learning. Find the full review here.
Grokking Deep Learning is a great introduction to Deep Learning that will teach you how to build Deep Neural Networks from scratch by using a first principles approach and getting you to code and understand the most basic building blocks of ANNs with very little math.
Begin your Deep Learning journey with one of the best books out there with Grokking Deep Learning. Read the full review here!
Foundations of Deep Reinforcement Learning is in our opinion the best book out there to get started on the topic of reinforcement learning. It provides an introduction to Deep RL that has both, greatly explained theory, and neat code implementations.
By the end of it you will know the theory and main concepts behind Deep Reinforcement Learning algorithms, how to implement them, as well the best practices and practical details of how to get RL to work. Find the full review here!
Pattern Recognition and Machine Learning is an advanced book, for graduates or Phds that already have experience in Machine Learning and Probability theory, and that are looking to deepen their knowledge in these topics through a Bayesian perspective.
If you are looking for a fun, well redacted, illustrated and complete book to master Bayesian Machine Learning, then definitely check it out. Find the full review here!
Practical Natural Language Processing provides a high overview of Natural Language processing that can suit both people with technical experience in the Data Science field, and those with no programming or tech background that want to understand how leveraging text data and data science can evolve their organisations and businesses to the next level.
Its is a text that provides a great introduction to NLP, and that is understandable to all audiences, not just techies. We love it. Check out the full review here!
Under the slogan ‘Make Neural Networks Uncool again‘ fastai is trying to democratise how the most valued weapon of Machine Learning is met by every day users. This awesome book demonstrates that any programmer with some Python experience can get amazing results using Deep Learning with very little math background, and a minimal time investment .
As so, it is mainly oriented towards coders with little experience of Machine or Deep Learning. We highly recommend it after some more general books on this section like Hands-On Machine Learning with Scikit-Learn & TensorFlow that want a practical approach to learning DL. Check out the full review!
Feature engineering is a crucial step in any Data Science/ML Pipeline, however most texts are dedicated to model building and training (the 20% of the previous paragraph), rarely covering the topic of feature engineering on its own.
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists covers the techniques for extracting and creating features out of raw data, so that your machine learning models can better understand them and therefore achieve better results. Read the full review here!
Generative Deep Learning is the core of technologies like GANs and poetry writer or pyschodelic image generators. Deep Fakes are also made using it. However, despite all the power of this technology it is hard to find good resources to learn about it.
Data Science in Production: Building Scalable Model Pipelines with Python provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust Data Science pipelines.
It will take your theoretical Machine Learning model building knowledge to the next level, teaching you how to take your analytical models to a productive environment from where real value can be extracted. Check out our full review here!
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