As per the trends, we are witnessing a surge in job openings in the machine learning sector. There are so many advancements that are being made in such a short time and for all the curious learners like you or any professional, it’s high time to keep yourself updated. The books that you will find here are tested by thousands of readers. If you are a coder, you would know that machine learning is the next big thing and these books will be a helping hand.
We will give you a brief review of the books mentioned in the table; however, you must keep a few factors in your mind before buying any machine learning book.
1. Choose the specialization
As there are a plethora of books that you will find online on the subject of machine learning, it’s quite necessary to find the one with the specialization that is related to your skillset. It will help you in skipping the detailed intro part that you are already familiar with.
2. High user ratings
While you are buying a book, it’s important to go through all the user reviews that you can find. The editor’s review is important, as well. It will give you a much wider perspective on the content and target audience of the book. If you are a beginner, it is even more important as you are going to learn the basics.
3. In-depth explainers
Various books are quite lengthy and cover almost all the aspects related to the concerned topic. For beginners, going for these books isn’t a wise decision. There are multiple options that you will find that offer to-the-point content without any unnecessary filler.
4. Type of cover
The book that you are getting will be available in various formats. If you are one of those people who like to maintain a bookshelf, we would recommend going for the hardcover edition. For those readers who are always on the go, you should settle for the Kindle or the paperback edition based on your preference.
Last but not the least, while you are searching for the best book that you can find on machine learning, you will encounter a range of options that will go as high as $60-$70. This is the correct range for professionals to invest in. If you are a beginner, you should try to find a more affordable option that doesn’t put any strain on your budget.
9 Best Machine Learning Books
|Best Machine Learning Books||Pages||Suitable for||Hardcover|
|Hands-on Machine Learning||856||Professionals||Paperback & Kindle||Check On Amazon|
|The Hundred-page Machine Learning Book||160||Beginners||Paperback & hardcover||Check On Amazon|
|Python Machine Learning||770||Beginners to practitioners||Paperback & Kindle||Check On Amazon|
|Machine Learning For Absolute Beginners||162||Beginners||Paperback & Kindle||Check On Amazon|
|Introduction to Machine Learning with Python||400||Beginners||Paperback & Kindle||Check On Amazon|
|Pattern Recognition and Machine Learning||738||Beginners to professionals||Hardcover||Check On Amazon|
|Machine Learning with R||458||Professionals||Paperback & Kindle||Check On Amazon|
|Machine Learning: A Probabilistic Perspective||1104||Beginners to professionals||Hardcover & Kindle||Check On Amazon|
|Machine Learning For Dummies||432||Beginners||Paperback||Check On Amazon|
Best Machine Learning Books: Reviews
Machine learning is one of those topics that automatically attracts curious minds. It’s the future and you must keep yourself updated with all the latest developments. The use of machine learning is basically to give your non-living smart companions a little brain of their own so they can perform a few tasks for you.
The technology will also be highly useful in searching for something particular from the vast database of your company. This book is one of the best guides you can settle for. It will also give you the relevant details on what marketing strategy you need to follow to pitch your idea. The algorithms used in this book will be enough for machine learning. Scikit-Learn is quite simple and easy to use where all the machine learning tools will be implemented very easily.
The other two algorithms that are discussed here are Keras and TensorFlow. The latter is used for the numerical operations and it will also be a bit more complex than the previous one. This API was created at Google and it is implemented on a number of applications that are opted by large-scale companies. The last one dubbed Keras is used as a deep learning API that will be integrated with the MCT, Theano, or TensorFlow.
This is one of those books that you must not miss upon if you have just started. The professionals who have gone through this guide expressed their wish to have this book when they started learning about machine learning.
The content that is provided here is not intended to be read just once to basically complete a book. As a professional, you will need to keep referencing from it in your projects. There are a few cons that you will get with this book and that’s the language used here is highly complicated and you will feel like not being able to grasp what you are reading.
If you ask whether you should go for it, we would like to say that if you are an enthusiast who loves to read about data science and advanced technologies that are being introduced in the machine learning world, you can get it. It will seem easier for you if you have some basic knowledge of mathematics and related topics. This Andriy Burkov reference to machine learning has managed to offer a very good balance between the visual appeal, the existing algorithms, and the explanations that are quite detailed and lengthy.
If you are in the market searching for a guide that can introduce you to machine learning, you can’t afford to add this one to the collection. The book is one of the best that offers a good compilation of the step-by-step guide and essential techniques that you will need to implement in your projects. The book is packed with real-life examples that will help resolve a plethora of issues that you might be facing. The book also features good visuals that are significantly helpful in easy learning. The other options that you will get in this list are not very keen on explaining the principles and just focus on giving you the step-by-step guide; this is not the case with this one. The author has managed to focus on the principles.
You don’t need to spare a single thought if you are already familiar with Python and want to learn the programming that is for machine learning. The book is focused on giving you the right elements to study the framework and techniques that will be needed to allow machines to grasp and learn the data trends. The content that you get here is suitable for beginners, as well as professionals who want to have a reference point to clear some confusion.
The basic concept of machine learning is that it starts with feeding the data into an algorithm and then it will allow machines to learn and eventually, get the desired result. It is neatly mentioned on the book cover using a pictorial representation. The curiosity of learning an all-new topic will be quite fruitful if you are using just the right tools to learn.
The fundamentals that you will get in this book will make a very concrete foundation on which the further topics will stand. Before you make your decision of getting this guide, it’s important to know that it is strictly recommended for beginners and if you have already crossed that level, it is not for you.
The book that’s listed here is the 2nd edition in the series and if you have already read the first edition, it is still the one you should go through. Not only all the chapters from the previous-gen are included here, but there are also some improvements and advancements that are included.
The reference guide will give you a step-by-step tutorial on a range of topics including downloading the datasets, binning, dealing with all the missing data, and the steps that you will need to follow to develop your very own machine learning model using Python.
As the companies now have so much data, it’s next to impossible to find what you need without any proper machine learning tool that will give exactly the data that you need and when you need it. Machine learning is also being widely used in several research projects that contribute towards making things a bit easier.
The author duo Andreas C. and Sarah Guido has successfully delivered a compilation of some of the best practical applications to implement ML. The examples that you will get here will be significantly helpful in future projects. The book is a perfect fit for you even if you have a beginner skillset in the Python language. When it comes to implementing machine learning, there is no limitation and all you need to do is explore your creativity.
The book will start with introducing the basics of machine learning. This reference guide uses Python and Scikit-Learn algorithms to demonstrate the ML programs. The authors have tried to offer you a practical implementation rather than explaining the statistical principles and maths behind it.
The book will also include a detailed guide for the process of dealing with the texts. There will also be a dedicated section for the tips and suggestions that you need to know to enhance your ML skills.
Machine learning is growing at a rapid pace and you must start learning right now. The primary reason behind the growth is the advancements that are made by the techniques and the algorithms that are used to implement data mining. The book starts with basic info on pattern recognition and there is no need to invest in any other resource if you want to start learning ML.
This updated text has all the updated developments and it will be a good investment if your focus is to make a successful career in this field. Pattern Recognition and Machine Learning is a 738-page thick book that will cover almost all the aspects of machine learning and pattern recognition.
The book also covers a wide range of practical examples that will help in future projects. The language opted by the author Christopher M. Bishop is quite simple and straightforward where you will not face any issue in grasping the content. All you need to do is have a basic knowledge of calculus and algebra. Mathematics is an integral part of machine learning and the book itself will provide you with a general and basic knowledge of the theories related to probability.
This algorithm that this book offers is quite expansive and easy to learn and will allow you an easy process to transform the existing data that you have into action. This action will be a perfect fit to introduce you to all the enhancements and advancements that you can make in the existing workflow.
No matter if you are a professional or the one who is just starting, the guide is the perfect fit. The author made sure that the content is enough when it comes to starting learning about this algorithm. The book will also include the history of machine learning and using an example, it will demonstrate how a computer learns from the data.
The reference guide by Brett Lantz will also include the forecasting of weather trends, numerical data in the financial sector, etc., it will use the regression method. The book will also include tips that you can follow to enhance the learnings that you have. The guide also covers Bayesian Methods, neural networks, decision trees, etc. in detail so that you don’t have to go through any extra hassle of finding a resource to go through these aspects in detail.
Kevin P. Murphy is the author of this advanced learning book and if you have just started learning this area of science, this book has got you covered. The focus that this book puts on is to offer you the techniques that you will need to learn to easily automate the process.
Once you complete this reference guide, you will be able to incorporate machine learning in your company’s data that will eventually predict data by understanding the patterns. There is also enough use of pictorial illustration that will also be significantly important in easy learning for beginners. The examples that are included here are also quite practical that will help professionals in treating this book as a reference guide.
The only prerequisite that you need to have is some knowledge of college-level maths. The programs that are mentioned in this guide are tried and tested in the MATLAB package. The language that is used in this guide is simple and straightforward.
There is also one thing that you need to keep in mind is that the focused audience for this book isn’t beginners. The guide is quite detailed with a thickness of 1104 pages. The topics are covered in detail and no other guide in this list is as detailed and comprehensive as this one.
Understanding the science behind machine learning is no joke. The book is from the ‘For Dummies’ series that is known for the explainers. The simplicity and detailed explanations that this book has to offer to make it a perfect fit for the curious learners who have just started.
The basic concepts are explained so well in this book and that’s also the primary reason professionals will find it a bit boring. If you are wondering why you need to learn machine learning, there are various reasons behind this. The world is moving quite fast and having a good machine learning setup is kind of mandatory. The tasks where you need to detect fraudulent activity, filtering the email spamming, providing real-time ads, etc., are impossible without ML.
The author duo John Paul Mueller and Luca Massaron has successfully delivered a guide where all the core concepts are explained in such a way that beginners don’t end up searching for any other guide to get the basics done. The book also has a dedicated chapter where the authors have pointed out all the daily-life implementations of machine learning science and how these applications make things easier for us.
Machine learning is the science that one shouldn’t ignore to learn if they are working in the IT industry. The books that we have mentioned here are the best ones that you will find in the market and we hope that you are able to make the right choice. If you are finding yourself in a dilemma and aren’t able to pick one, there’s nothing to worry about as we are going to recommend two of the above-listed books.
- For beginners, the best pick is Machine Learning For Dummies. The primary focus of this guide is to introduce beginners to the basics of ML.
- If you are a professional, we would recommend picking up Machine Learning: A Probabilistic Perspective. It is the most comprehensive and thorough guide you can find.