Machine Learning algorithms allow us to build intelligent systems which can learn from past experience to give accurate results. Healthcare, defence, financial services, security services, among many others, use Machine Learning in their applications today. The applications of ML have shown a drastic increase over the years and as a result, the career opportunities are bound to shoot up as well. If you are someone who has no prior knowledge about ML, but are interested to learn new concepts and maybe go on to start a career in this field, you must take a look at the following seven Machine Learning books. They will be quite helpful in your future endeavours:
Best 7 Books on Machine Learning for Beginner and Experienced for 2020
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd Edition)
- Machine Learning with Python
- Machine Learning: The definitive guide
- Machine Learning: The Ultimate Beginners Guide to understanding Machine Learning Concepts and Techniques
- Machine Learning for Hackers
- Machine Learning: The art and science of algorithms that make sense of data
- Machine Learning with R: Expert techniques for predictive modelling
- Artificial Intelligence and Machine Learning for business
- Python for Probability, Statistics and Machine Learning
- Neural Network and Statistical Learning
1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd Edition)
By Aurélien Géron
A series of recent breakthroughs in Deep Learning have boosted the entire field of Machine Learning. Programmers who had no prior knowledge about this technology are able to use simple, efficient tools to implement programs capable of learning from data. The book also uses concrete examples, minimal theory and Python frameworks. The author helps you gain an understanding of various concepts and tools that will help you build intelligent systems. You will also learn a variety of techniques such as linear regression and deep neural networks. There are simple exercises in each chapter that will allow you to apply your newly gained knowledge. Basic experience in programming will help you understand the book in a better way. The book also helps us learn the use of TensorFlow library and building neural nets.
2. Machine Learning with Python:
The Ultimate Beginners Guide to Learn Machine Learning with Python Step by Step
By Ethan Williams
Published in August 2019, Machine Learning with Python provides us with an insight into the world of Big Data and Machine Learning. We are living in a world wherein gigabytes of data are generated on a daily basis. There are certain problems that arise when there is such a large volume of data being generated and Machine Learning provides solutions for the same. It answers questions such as- ‘How can I apply ML in my enterprise’, ‘what the internet thinks of my academic research’, and many other questions. It is the perfect start to understanding how programming languages are used in Machine Learning for development and advancement.
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By Oliver Tensor
Published in August 2019, this book covers a range of topics. The aim of the book is to provide a 360-degree view of the importance and the fundamentals of Machine Learning and Artificial Intelligence Technology. At the end of the book, you will gain an in-depth understanding of the different popular AI tools available in the market. The book is written in an easy to understand language and is thus suitable for beginners as well. It also teaches us the different ML Algorithms and how machines are able to train themselves to perform in a better manner. Virtual tools like Siri and Alexa have become part of our everyday lives and the book teaches us how these technologies function.
4. Machine Learning: The Ultimate Beginner’s Guide to Understanding Machine Learning Concepts and Techniques (Beginners, Intermediate & Advanced)
By Adam Jaxon
Most people miss out on starting their career in ML due to having access to the wrong resources. This unique book talks about all the methods and the history of Machine Learning. Some of the topics covered through the course of this book are application of ML, Statistical learning, Natural Language Processing, different learning methods, Deep Learning, and many more. This book is written in such a way that it will bring out the real passion in you, rather than going into unrelated data and math.
By Drew Conway, John White
Each chapter in this book focuses on a specific problem in Machine Learning. It covers various ML problems such as classification, prediction, optimisation, and recommendation. It uses R Programming language and teaches how to analyse sample datasets and also how to write simple Machine Learning algorithms. This book is ideal for programmers from any background whether business, government or academic research. The authors have also provided hands-on case studies, instead of a traditional approach. An example of what the book teaches is the use of linear regression techniques to predict the number of page views for a set number of websites.
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By Peter Flach
This book does justice to the field’s incredible richness and it does so without losing sight of the unifying principles. It is one of the most comprehensive ML books and Peter Flach uses a clear, example-based approach. He starts off by discussing how a spam filter works, which gives an immediate introduction to Machine Learning in action with minimum technical fuss. The book provides case studies filled with variety and increasing complexity, along with well-chosen illustrations and examples. A wide range of statistical models, metrics factorisation and ROC Analysis is also covered in the book along with relevant background material and an introduction to new and useful concepts has also been mentioned. It is a well-balanced book that provides key pointers for revision as well and sets a new standard for other introductory Machine Learning books.
By Brett Lantz
Published in April 2019, the third edition of a best seller book talks about how to harness the power of R to build flexible, effective and transparent Machine Learning models. A powerful set of ML methods are offered by R which will help us easily gain insight from our data. The book mainly targets data scientists, students and others who want a precise guide to Machine Learning with R. Forecasting numeric data, estimating financial values using regression methods, evaluating your ML models and improving their performance are some of the topics that are covered in this book.
So these were the top 10 books on Machine Learning that we recommend to start with. Upskill with Great Learning’s PGP- Artificial Intelligence and Machine Learning to master the domain.
By Scott Chesterton
Published on July 19, 2019. If you wish to implement AI and ML to your business but don’t have a definite idea about how this can be done, this might be the perfect book for you. There are endless opportunities and those can be accomplished with the help of the book. There are various tips and techniques mentioned in the handbook that can be implemented in your business. The book covers an array of topics, including, the basics of AIML, the challenges you may face in your business, how to overcome these challenges, and more.
By Jose Unpingco
Updated to feature Python version 3.7, the book covers key ideas that link probability, statistics, and machine learning. Python codes and their associated Jupyter/IPython notebooks are provided as downloads. With the help of multiple analytical methods and Python codes, meaningful examples are provided. Thereby connecting theoretical concepts to concrete implementations. You will require basic knowledge in Python programming to understand this book and make the best out of it. Mathematical topics such as convergence in probability theory are developed and illustrated with numerical examples.
By Ke-Lin Du, M. N. S. Swamy
The book provides a detailed introduction about Neural Networks and Machine Learning in a statistical framework. It is a comprehensive resource for further research and explores popular models and approaches. It gives a number of examples; this helps the reader understand a more practical approach to the content. Some topics covered are multilayer perceptron, probabilistic Bayesian networks, fuzzy logic, recurrent neural networks, and more. It is suitable for academic and technical staff, graduate students, as well as researchers.
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