Artificial Intelligence and Machine Learning have become one of the hottest and most popular domains in the computer science and Future tech industry. So, it highly sought machine learning books that you should not miss to read in 2023.
Every other company around the world is trying to implement Machine learning for better efficiency and transformation, or they are taking up machine learning projects for solving other company’s issues and developmental goals.
If you are that one person who is looking to explore this domain and make take up a new challenge, then we have listed out a few crucial artificial intelligence books and Machine Learning books to start with your learning journey.
Machine Learning Books:
It is an introductory book to machine learning, which is targeted at people who don’t have much knowledge or experience in Python.
It will teach you how to build your own machine learning solutions through various methods complemented by multiple sets of examples.
This can be called as the best textbook for beginner machine learning engineers or practitioners.
This book provides basic theoretical concepts of artificial intelligence. Beginners can consider this book as a complete reference. It is beneficial for students studying undergraduate or graduate-level courses in Artificial Intelligence.
The latest edition gives you in-depth information about the changes that have taken place in the domain of artificial intelligence from its last edition.
The tremendous practical implications of AI like actual speech recognition, machine translation, general robotics are all well explained in this book.
3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Python Machine Learning is an excellent practical book that includes multiple examples of code. This book helps you in the natural understanding of the concepts and tools for developing and building intelligent/advanced systems.
You will learn several techniques and ways to start with basic linear regression and progress towards deep neural networks.
With the help of practical exercises within each chapter to apply your learnings. It would be best if you had a basic understanding of programming.
This comprehensive and conceptual book on the language ‘R’ will help you get insights from complex datasets and apply the correct algorithms for solving specific problems.
You will learn how to apply Machine Learning methods to deal with main tasks like forecasting, image categorization, prediction, and clustering.
Machine Learning with R will help you to acquire a brief understanding of a broad scope of subjects but can be possibly less suitable for those who want more in-depth insights in a particular field.
This book is a gem. It is a classic practical guide to get started and execute on Machine Learning within a few days without compulsorily knowing much about ML priorly. Linkedin superstar Andriy Burkov authors it.
The first five chapters will get you started, and the next few sections provide you the confidence to pursue more advanced topics. ” rA wonderful book for engineers who want to learn ML in very little time without making efforts of learning through the professional degree program.
This book was written by the creator of Keras- François Chollet. Keras is one of the most well-known machine learning libraries in Python.
This book starts gently and then goes deep into the practice mode, gives multiple pieces of code you can use straight away, and has many tips in general that can help you in your quest for deep learning. A significant must-read book for people who have knowledge in deep learning.
This deep learning book offers a mathematical background and relevant concepts in linear algebra, probability, and deep learning techniques.
The book describes many important deep learning techniques mostly used in the industry, which include deep feedforward networks, convolutional networks, optimization algorithms, sequence modeling, and practical methodology.
This book also offers details on research-related information like linear factor models, structured probabilistic models, autoencoders, partition function, etc.
This book is specially dedicated to practitioners who already have a good understanding of machine learning and trying to become an expert in this field.
If you are more mathematical-oriented, it is the best book you will read with more machine learning methods that are most advanced in nature.
It is difficult to complete the book at once, but It has been proven to be the best and most comprehensive reference for Machine learners.
This popular book has been authored by Tariq Rashid. It’s a gradual journey towards the mathematics of neural networks. Through the Python programming language, you can create your own neural network with the help of this book.
In Part 1, different mathematical concepts of neural networks are discussed. Part 2 is thoroughly practical, which helps you to learn the Python language and helps you to create your own neural network recognizing human handwritten numbers and networks made by professionals.
Part 3 has extended the ideas further.
This book is recommended for every absolute machine learning beginner. This book gives a practical and high-level priority to the practical as well as statistical concepts found in machine learning.
There is no coding experience required and all the concepts are explained in plain and simple English language. You will find all the core algorithms introductions are clearly explained and visual examples are added to make it easy and engaging for a home follow-up.
This major new release consists of multiple features and topics which are missing in the first edition, including Cross-Validation, Data Scrubbing, and Ensemble Modeling. This book is an excellent restructured and revamped version of the First Edition and not a sequel to the first edition.
The authors of this book Jeremy Howard and Sylvain Gugger, known as the creators of Fast ai, show you the perfect way to train a model directed towards a wide array of tasks using fastai and PyTorch.
You’ll also go deep progressively further into the critical subject of deep learning theory and gain complete knowledge and working of the algorithms behind closed doors.
This hands-on guide illustrates, programmers experienced with Python can attain magnificent results in deep learning with minimal math background, not a very large amount of data, and very little code.
The author offers an all-encompassing and current exploration of AI fundamentals, covering a wide range of topics including GANs, NLP, prompts, deep learning, and AI ethics. The presented information is presented in a clear and comprehensible manner, facilitating easy learning for individuals without any prior experience in the field.
This book offers a thorough examination of the fundamental principles underlying Artificial Intelligence, providing a detailed breakdown of Generative AI, NLP, Prompts, and Deep Learning techniques. It also includes practical examples and interactive exercises designed to enhance your AI proficiency. Furthermore, it explores the ethical dimensions and considerations surrounding AI technology, as well as providing insights into the future of AI and its potential influence on industries and society. Lastly, the book delves into the art of creating captivating content using AI-driven prompts.
In her book, Chip Huyen, co-founder of Claypot AI, thoroughly examines the impact of design decisions on the overall effectiveness of your system. These decisions encompass various aspects, such as training data processing, feature selection, model retraining frequency, and monitoring practices. By presenting real case studies supported by extensive references, the book offers an iterative framework to address these considerations.
The book serves as a valuable resource for addressing the following scenarios:
- Solving business problems by engineering data and selecting appropriate metrics.
- Streamlining the development, evaluation, deployment, and updating of models through automation.
- Implementing a monitoring system to swiftly identify and resolve issues that may arise with models in production.
- Designing an ML platform that caters to diverse use cases.
- Building responsible ML systems in line with ethical considerations.
To grasp machine learning concepts, it is essential to have a solid foundation in mathematical tools such as linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. However, these topics are typically taught in separate courses, making it challenging for data science or computer science students and professionals to efficiently acquire the necessary mathematical knowledge. This textbook aims to bridge this gap by presenting the mathematical concepts with minimal prerequisites, making it self-contained and accessible.
The book seamlessly connects mathematical principles with machine learning by deriving four key machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. By providing these derivations, students and individuals with a mathematical background can use them as a launching pad to delve into more advanced machine learning texts. Moreover, for those encountering these mathematical concepts for the first time, these methods serve as valuable tools for developing intuition and practical skills in applying mathematical concepts.
Each chapter of the book includes worked examples and exercises to facilitate comprehension and reinforce learning. Additionally, the book’s website offers programming tutorials to further support readers in their understanding and application of the concepts discussed.
Although possessing expertise in machine learning doesn’t necessarily enable one to develop a Turing Test-proof android akin to the one depicted in the film Ex Machina, it does represent a form of artificial intelligence that holds great promise in swiftly identifying opportunities and solving problems on a significant scale. Mastery of the principles of machine learning equates to mastering a substantial component of our technological future, while also paving the way for exciting career opportunities. These careers encompass various domains, including fraud detection, optimization of search results, real-time advertising, credit scoring, construction of precise and advanced pricing models, and countless other possibilities.
Acquire a solid grasp of the core principles behind deep learning and machine learning employing concise explanations and abundant code examples. Develop proficiency in applying graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learning techniques. Stay up to date with the latest advancements in machine and deep learning methodologies. In the book “Deep Learning with TensorFlow and Keras,” you will be instructed in the concepts of neural networks and deep learning techniques employing the TensorFlow (TF) and Keras frameworks. This detailed book will guide and equip you with the knowledge and skills to develop robust deep-learning applications using the most potent, widely adopted, and scalable machine-learning stack currently accessible.
We hope all the required Machine learning books, are mentioned in this list. While there are tons of free e-learning Artificial intelligence and machine learning courses on the internet, books are still relevant even in these days. We would love to get more such alternative books from our readers in the comments section below.