If you're looking to get started in machine learning, here are a few great books that can help with basic concepts.
Machine learning is a fascinating field that can be used for all sorts of applications. It's no wonder that it's becoming more and more popular every day.
If you're looking to deepen your understanding of machine learning or to become a machine learning engineer, these books will give you the tools you need to succeed. With their help, you'll be able to apply this powerful technology to your projects.
Read one of these great machine-learning books today!
How We Choose These 6 Books for You
It can be hard to know which books to read when you're trying to learn a new skill.
With so many books on the market, it's hard to know which ones are worth your time and money. And once you've chosen a book, it can be difficult to know where to start and how to keep progressing.
We've got you covered. Our list of the Best Machine Learning Books will help you choose the right book for you, and our step-by-step guides will show you how to make the most of it.
Best for Scientists
This amazing book provides step-by-step instructions for creating successful machine-learning applications – no complicated math required!
1. Introduction to Machine Learning with Python by Andreas Müller and Sarah Guido
What Is This Book About
With this amazing book, authors Andreas Müller and Sarah Guido provide step-by-step instructions for creating successful machine-learning applications – no complicated math required!
Whether a novice programmer or a seasoned technician, Introduction to Machine Learning with Python will take you from zero skills to understanding and implementing machine learning in no time.
Authors Müller and Guido keep their emphasis firmly on the practical aspects: just follow their clear instructions and before you know it, you’ll have created a neural network that can detect handwritten digits or a random forest used to classify astronomical objects.
Although there are topics that demand some technical concepts, they’re explained in an easy-to-follow manner so anyone can understand. You won’t find yourself struggling to remember sophisticated formulas or equations; instead, you’ll get hands-on exercises that allow you to apply your knowledge immediately.
Why You Will Love It
Introduction to Machine Learning contains the fundamental concepts and applications of machine learning that you need to understand to make the most of your data. You can learn about the advantages and shortcomings of commonly used machine learning algorithms, as well as how to represent data effectively and which aspects to focus on. Furthermore, it also offers guidance on advanced methods for model evaluation and parameter tuning, including pipelines for chaining models, encapsulating your workflow, methods for working with text data that include text-specific processing techniques, and suggestions to improve your machine learning and data science skills.
Best for Tools and Techniques
This book is packed full of all the tools and techniques you need, starting with the basics of linear regression and moving right up to deep neural networks.
2. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
What Is This Book About
This book is packed full of all the tools and techniques you need, starting with the basics of linear regression and moving right up to deep neural networks. You don’t have to be a coding master to use this book either – all you need is some programming experience. During your journey through this book, you’ll work with concrete examples, and exercises in each chapter will help apply what you’ve learned. This great guide has been written in a friendly way – perfect for beginners looking to crack into machine learning! So if you’re ready to build smart systems using two of the most popular Python frameworks out there – Scikit-Learn and Tensor Flow – then Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron is just the book for you!
Why You Will Love It
This book provides a comprehensive insight into the fundamentals of Machine Learning. The author addresses topics like supervised and unsupervised learning supervised random forest regression, and classification model tuning, as well as more advanced topics such as artificial neural networks and deep learning. He explains everything in an easy-to-understand way with clear examples, making it perfect for any beginner or experienced user of ML. Additionally, this book offers an in-depth analysis of the core principles behind ML, helping you gain a thorough understanding of how it works so that you can have greater success using it in real-world applications.
Best for Beginners
This Third Edition has been specifically designed for absolute beginners and goes the extra mile with extended chapters, quizzes, and tutorials that are sure to help boost your understanding.
3. Machine Learning for Absolute Beginners by Oliver Theobald
What Is This Book About
Machine Learning for Absolute Beginners by Oliver Theobald provides a high-level introduction to machine learning, along with downloadable code exercises and video demonstrations. This Third Edition has been specifically designed for absolute beginners and goes the extra mile with extended chapters, quizzes, and tutorials that are sure to help boost your understanding. Even better: each edition comes with free resources you can download not included in the Second Edition, plus supplementary online video tutorials that walk you through coding models in Python. Plus – it’s all written in a friendly tone so it won’t feel daunting!
Why You Will Love It
This book takes you from learning how to download free datasets to building your first Machine Learning Model with Python. You'll learn what tools and machine learning libraries you need, data scrubbing techniques such as one-hot encoding, binning, and dealing with missing data, and how to prepare data for analysis utilizing k-fold Validation. You'll also learn regression analysis to create trend lines, k-Means Clustering to find new relationships, the basics of Neural Networks so you can reduce Bias/Variance in your model and improve its accuracy, as well as Decision Trees for classification. At the end of this book, you will have built a Machine Learning Model that predicts house values!
This book is suited for those curious about machine learning, as well as those who need specific guidance when creating powerful models that can perform practical tasks.
4. Machine Learning for Dummies by John Paul Mueller
What Is This Book About
Machine Learning for Dummies by John Paul Mueller makes learning machine learning straightforward and enjoyable! The latest edition of this invaluable guide is updated to accommodate everyone, from novices to experts. Machine Learning for Dummies doesn't require prior experience with programming languages like Python or R—it's designed to let you in on the ground floor. With an easy-to-read style and practical examples, this book is suited for those curious about machine learning, as well as those who need specific guidance when creating powerful models that can perform practical tasks. Of course, it takes a look at the underlying—and fascinating!—maths principles that power machine learning, delving into topics such as linear regression; however, it will ease your concerns if maths isn’t your strong suit – the book doesn’t require any special math proficiency to understand. What's more? Downloadable R source code and in-depth explanations are included to help you unravel the mysteries of Machine Learning!
Why You Will Love It
This book not only provides an introduction to the exciting technology of machine learning but also breaks down complex concepts and data into more simplified explanations that everyone can understand. You will learn the fundamentals of AI, uncover best practice examples of how to effectively use machine learning algorithms, explore real-world datasets and ethical approaches to data use, and even get your hands on development exercises to hone your skills - all within a convenient and friendly guide! Whether you’re an experienced developer or someone new to the world of data science, this is the perfect book for anyone looking to dive into machine learning and make their mark in this rapidly evolving field.
Best for Engineers
This book shows how to apply Agile methodologies for fast prototyping and working with stakeholders while also providing valuable insight into planning for long-term success.
5. Machine Learning in Action by Ben Wilson
What Is This Book About
This book shows how to apply Agile methodologies for fast prototyping and working with stakeholders while also providing valuable insight into planning for long-term success. All of your questions about creating deployable and maintainable ML systems can be answered within its pages. Everything is broken down into understandable chunks so that no matter what level of learning you have—you’ll never feel overwhelmed or confused when reading it.
The friendly tone used makes the book approachable and easy to digest, speaking to its audience engagingly and helpfully. Whether you’re just starting or have been tinkering with machine learning for years, this book has something for everyone. It can even be used as a time-saving reference guide for when a tricky problem arises.
Why You Will Love It
This book will help you take on data science problems with confidence. You’ll learn to evaluate solutions and establish the right project scope to do the most with your budget. You’ll discover process techniques that minimize wasted effort and maximize efficiency, assess projects using standardized methods, select the most appropriate technologies and tools for a given project, make code more readable and maintainable, and automate troubleshooting and logging. You’ll gain the skills to take on any data science challenge head-on!
Best for Basics
This easy-to-follow introduction is ideal for analytics practitioners, engineers, or anyone looking to understand the basics of machine learning.
6. The Hundred-page Machine Learning Book by Andriy Burkov
What Is This Book About
With the expert guidance of best-selling author and AI specialist, Burkov, this fun, and friendly guide introduce the essential topics of machine learning in 100 pages or less—enough to get you ready to implement the knowledge into your real-world projects.
With a focus on simplicity and readability, this easy-to-follow introduction is ideal for analytics practitioners, engineers, or anyone looking to understand the basics of machine learning. You'll navigate through key concepts such as algebraic linear models, naive Bayes classifiers, decision trees, and more with clear illustrations that help explain complex ideas. What's more, there's motivational advice throughout from Andriy so you can stay focused on moving forward in your learning journey.
Put theory into practice swiftly with The Hundred-page Machine Learning Book by Andriy Burkov—the ultimate bridge between technical theories and application.
Why You Will Love It
This book is a fantastic introduction to machine learning from a well-known and highly respected practitioner, Andriy Burkov. You'll benefit from his perfect balance of intuitive visualizations, comprehensive mathematical descriptions, and easy-to-understand explanations - whether you are just starting in the field or already have some experience. You can use this guide to help you get started with machine learning quickly, regardless of your current level of knowledge.
Best Machine Learning Books FAQs
You might be wondering, what are the best machine learning books for me?
It can be tough to know where to start when it comes to picking out the best machine-learning book for you. With so many different options on the market, it can be hard to know which one will help you learn and grow in this field.
Check out our most frequently asked questions about Best Machine Learning Books. We've compiled a list of the most commonly asked questions and provided answers to help make your decision easier.
Which book is best for machine learning beginners?
For those interested in machine learning, there are several excellent books to choose from.
Hands-On Machine Learning with Scikit-Learn and TensorFlow: This is an ideal book for beginners who want to get started with practical machine learning projects quickly. Written by Aurélien Géron, it provides step-by-step instructions and detailed explanations on how to use the popular open-source libraries Scikit-Learn and TensorFlow for machine learning tasks. It covers the fundamentals of statistical modeling and supervised and unsupervised learning algorithms (including deep neural networks), along with best practices for model selection, tuning, regularization, and optimization.
Machine Learning Yearning: Unlike Hands-On Machine Learning which is more of a tutorial guide on using these libraries, this book written by Andrew Ng focuses more on the theoretical side of things like goal-setting strategies needed to create powerful ML systems. It also discusses topics such as debugging models and developing software that utilizes AI/ML models efficiently without having to understand each algorithm's inner workings in depth.
Python Machine Learning: This book written by Sebastian Raschka offers a comprehensive introduction to understanding overall concepts regarding Artificial Intelligence while providing detailed examples using Python's popular library scikit-learn (as well as other frameworks). Additionally, it covers various topics related to data preprocessing techniques used in conjunction with machine learning algorithms commonly used within scientific computing applications as well as its usage within real-world areas such as Computer Vision or Natural Language Processing tasks utilizing Deep Neural Networks.
Are machine learning books worth it?
Yes, machine-learning books are worth it!
At their core, machine learning books provide insight into an ever-evolving field of technology. In today's world, advancements in machine learning have become increasingly important—as they impact a range of fields from healthcare to finance to robotics and more. As such, having access to the latest research and advances in this area can be incredibly valuable for any interested person or organization.
When it comes to the actual contents of a good machine learning book, you'll find that these volumes will typically contain detailed information on the various algorithms used in developing AI systems as well as their applications across fields such as computer vision and natural language processing. Furthermore, many specialized texts will dive deeper into particular areas such as deep reinforcement learning or neural networks with diagrams and related coding examples.
In addition to providing powerful insights into cutting-edge technologies, certain selections may also be written by renowned experts whose experience and opinions carry immense weight in this progressively important field. This could include thought leaders like Andrew Ng or Yann LeCun who have both made significant contributions to the development of artificial intelligence over decades of work—making these resources invaluable even if one simply wants to understand how leading figures think about specific problems associated with AI development.
Finally, most modern editions come complete with online sources such as interactive tutorials available free on websites like Coursera designed specifically for readers looking for hands-on experiences when exploring their favorite topics within AI engineering.
Is AI or ML easier?
The answer to this question depends on a variety of factors, including the individual's education and experience. Generally speaking, however, Artificial Intelligence (AI) is easier to learn than Machine Learning (ML). AI is typically used for general-purpose tasks like decision-making, reasoning, planning, and problem-solving. ML focuses more on learning from data sets to improve performance at certain tasks; thus it requires more advanced skills in mathematics and computer science than traditional AI techniques do.
Can I learn AI and ML on my own?
Absolutely - with the right tools, resources, and motivation, anyone can learn AI (Artificial Intelligence) and ML (Machine Learning) on their own. Fortunately, with the rise of internet access and technology over the past decade, acquiring information related to AI & ML has become easier than ever before.
What should I study first AI or ML?
The decision of what to study first when it comes to Artificial Intelligence (AI) and Machine Learning (ML) can be difficult, as both are important and cutting-edge fields of technology. Ultimately, this decision should depend on your interests and preferences.
If you're interested in the theoretical side of AI or ML, then you should focus on AI first. Here, you'll learn about complex algorithms such as neural networks, support vector machines, and natural language processing – understanding these concepts is key for pursuing an education in machine learning later on down the line. Additionally, exploring AI will help familiarize you with basic computers science principles such as algorithms and data structures.
On the other hand, if you have more practical goals in mind – like creating sophisticated applications or leveraging predictive analytics for business intelligence purposes – then ML would be a better place to start for your studies. In this case, building working models through analytical problems will give you immediate feedback from the system allowing you to gain experience quickly while practicing coding concepts at the same time. Plus starting with ML rather than AI allows a much quicker path toward creating advanced projects which can lead to job opportunities earlier on down the road.
Ultimately though while there is no wrong answer when deciding between studying AI vs ML first it’s important to note that knowing both topics should become essential regardless of your end goal so don’t forget that investing time into both eventually could create massive opportunities later on down line!
Are 6 months enough for machine learning?
No, six months is not enough time for a comprehensive understanding of machine learning. Machine learning is a vast and complex field that requires a great deal of commitment and patience if one expects to achieve success. It takes practice, continuous study, and experimentation to master the subject.
The amount of time you will need to spend on machine learning depends largely upon your knowledge level when beginning the process. If you are starting from scratch or have minimal knowledge or background in algorithms or coding, then it may take much longer than six months to acquire adequate expertise in the subject matter. However, if you’ve been studying computer science fundamentals or software development for some time already then it might be possible to dedicate yourself rigorously enough over those 6 months to become fully proficient at machine learning by then (though even this would require considerable dedication).
What is the 80/20 rule in machine learning?
The 80/20 rule in machine learning is a concept that has become increasingly popular over the past few years. It is based on the idea of Pareto’s Principle which states that “for many outcomes, roughly 80% of effects come from 20% of the causes.” In terms of machine learning, this means that 20% of models will typically account for 80% of your data and predictive performance.
This principle can be applied to several different aspects of machine learning such as feature selection and model selection. For example, when building a supervised-learning dataset you may need hundreds or even thousands of features to capture all necessary trends and patterns in your data however it stands to reason that only a small subset will be essential for good performance (likely around 15%). By using feature selection methods like Principal Component Analysis or Recursive Feature Elimination you can determine which features are most significant and thus identify the ‘vital few’ quantitative variables instead of wasting time trying to tune an unnecessarily large data set with minimal improvement.
What is the best age to learn AI?
The best age to begin learning AI and Machine Learning (ML) depends on the individual. Generally speaking, there is no one-size-fits-all answer and it’s up to a person’s ambition and interests.
Some people may have a natural aptitude or interest in technology from an early age; these individuals could potentially start mastering ML even earlier than others - as early as 6th grade! With the advent of 3D printing, kids are even able to experiment with robotics at home if they so choose.
At the same time, for those wanting to engage with ML more seriously - say for college-level coursework - high school can be a great starting point. For students already enrolled in AP Computer Science classes or taking independent tech courses like Codecademy, this can serve them well by providing a basic foundation that might give them an edge when applying for graduate programs such as Artificial Intelligence (AI). Additionally, universities often offer coding clubs and activities that provide hands-on experience while teaching fundamentals of programming languages like Python which are commonly used when dealing with ML algorithms.
In short, there is no definitive “best age” to learn AI but rather a personal journey depending on individual goals and ability levels. Through software resources like online courses or hackathons hosted by many universities today coupled with access to technical professionals through places such as LinkedIn, anyone can begin filling knowledge gaps so that their dreams become reality!
Can I learn ML without coding?
Yes, you can learn Machine Learning (ML) without coding! This is possible through the use of automated ML tools that allow you to develop ML models without having to code. With these tools, you can quickly and easily create predictive models with minimal effort.
However, it's important to note that there are still some basic programming concepts and algorithms involved in machine learning, even when using an automated tool. Understanding these basics will make your work more efficient and effective as a non-programmer.
Moreover, while learning machine learning without coding can be done relatively easily by automated ML tools, the real value comes from combining code with those tools. For example, if you eventually want to move towards advanced topics such as natural language processing or deep learning then understanding coding languages (such as Python for deep learning) will become a necessity to maximize your understanding of the subject matter at hand.
In conclusion, yes it is possible to learn Machine Learning without coding but one should still have a general knowledge of related programming concepts and algorithms so they can get the most out of their experience with an automated ML tool.
Is Python enough for AI and ML?
Python is an essential tool for AI and ML, but it is far from being enough. Python’s primary advantage lies in its flexibility; it can be used for a wide range of applications, including ML. Its syntax is also relatively straightforward to learn, which makes it a popular choice for developers of all skill levels.
Despite its popularity, however, there are many areas where other languages are better suited than Python. For instance, some niche languages such as R offer powerful statistical analysis capabilities that give them an edge in the world of data science while Go provides high performance with concurrent programming features that make it well-suited to large-scale distributed systems like Kubernetes or Docker clusters.
When applied to AI and ML specifically, Python shines with its huge collection of libraries built over the years by developers worldwide - libraries such as Scikit-Learn or TensorFlow provide ever-growing collections of specialized algorithms designed to overcome common problems faced by machine learning practitioners on a day-to-day basis.
Deciding on the best machine learning books can be tough with the vast number of options available to learn machine learning systems, practical machine learning tools, and machine learning techniques. Some of them cover very specif subjetc such as basic probability theory introduction or machine learning models.
After reading through thousands of reviews, we’ve compiled a list of what we believe are the top contenders. If you’re interested in starting a great machine learning journey by learning more about machine learning, any one of these books would make an excellent addition to your library. And if you’re unsure where to start, we suggest checking out our top pick: “Introduction to Machine Learning." With over 1,000 pages of content, this book provides readers with a broad overview of different machine-learning concepts and methods.
Another great book to begin understanding machine learning is Programming Collective Intelligence by Toby Segaran.
So whether you're just getting started or looking for something more advanced, "Introduction to Machine Learning" is a great place to begin your journey. Click on the buttons above and pick up your copies now!