Best Books for Machine Learning

When you plan to get an AI ML certification, you need all the essential sources to learn from for more excellent knowledge. And it is a known fact that machine learning has been helping us run tasks in an automated manner. It made things more manageable than before, and it is being used in complex tasks too. ML has numerous applications in different fields, beginning from space research to digital marketing and many others. By learning, machine learning and artificial intelligence will enhance your job opportunities. Artificial Intelligence also has its routes in the machine learning subject. 

 

It is the best time you can begin studying machine learning. Of course, this is a complex subject but can be learned quickly. It is one of the highest-paid fields that make this choice more feasible. And to start a career path in machine learning, you need to begin with the right source. To help you with the beginning, we have picked up some of the best machine learning books to make your learning easier. 

Natural Language Processing with Python

Natural language processing is where you can begin your learning as it is the backbone of the subject. This book consists of language processing that includes the python programming language. It guides users into using the NLTK, part of the python libraries. The book is addressed by Steven Bird, Ewan Klein, and Edward Loper. 

 

This book consists of robust python codes that demonstrate the clear NLP in a precise manner. It makes easy access to the dataset analysis that deals with the unstructured data, many NLP-oriented aspects, and the linguistic structure in texts. 

 

Topics that are Covered: 

  • How the Human Language works in the process. 
  • The integrated techniques from linguistics and artificial intelligence. 
  • Get access to the linguistic data structures. 
  • Not to forget the traits of the Natural Language Toolkit (NLTK) in this book. 
  • Linguistics databases and the parsing, semantic analysis. 

 

Understanding Machine Learning

This book offers the introduction of a complete structure on machine learning. It is divided into algorithm paradigms of machine learning and its fundamental theories along with mathematical derivations. The authors of this Book are Shai Shalev-Shwartz and Shai Ben- David. 

 

You can find numerous machine learning topics in an easy to understand way. Everyone can start learning through this book ranging from computer science students to very beginners in the field. 

 

Topics that are Covered: 

  • The computational complexity of machine learning
  • Convexity and stability in the topics 
  • ML algorithm, Neural networks, and the approach of PAC-Bayes 
  • Structured output learning

Machine Learning for Dummies

The authors of this Book are John Paul Mueller and Luca Massaron. The book’s goal is to make the readers understand the basic concepts and the theories of machine learning. Also, you can find the book concepts explained practically by conjoining them with real-world machine learning applications.

 

 

 

Python and R code are used to demonstrate the train of machines for finding the patterns and analyzing the results. It also consists of the ML facilitation regarding fraud detection, email filters, internet ads, web searches, and many more. 

 

Topics that are Covered: 

  • Machine learning techniques and data preparation 
  • About the machine learning cycle
  • Details regarding the training machine learning systems. 
  • For drawing the reasonable outcomes using the machine learning methods

Machine Learning In Action

The author of this book is Peter Harrington. It is preferred by different people, from beginners to professionals. It does not include only the details of the machine learning techniques but a more profound underlying concept. 

 

Developers can use this book to write their programs meant to acquire the data while aiming for analysis. There is a deeper conceptualization that discusses the forming of algorithms based on machine learning techniques. Most of the examples and programs that are used in this book are based on python code. 

 

Topics that are Covered: 

 

  • Machine Learning Basics
  • MapReduce and Big data
  • FP-Growth
  • Tree-based and logistic regression

Machine Learning with TensorFlow

The author of this book is Nishant Shukla. It is a symbolic math library and is considered the best data science python library. 

 

These concepts are used for machine learning applications, mainly neural networks. Readers can obtain clear explanations of the machine learning concepts and the experience of practical coding. 

 

You can learn all the ML basics with clustering, traditional classification, and prediction algorithms. The book consists of the more profound learning concepts that help the reader use the open-source TensorFlow library to write the codes. 

 

Topics that are Covered: 

  • Autoencoders, deep learning, linear regression 
  • Reinforcement learning, recurrent, convolutional
  • Reinforcement neural networks. 

Python Machine Learning

 A Technical Approach to Machine Learning for Beginners

The author of the book is Leonard Eddison. If you are a beginner, this is the best book for you to learn from the basics. You can also understand the more profound importance of the subject in it. This book also consists of concepts on different machine learning branches and their numerous applications. It will help you in finding easy ways to code even to accomplish a few complex tasks. 

 

You can also find the details of the python programming fundamentals. By following the concepts and instructions, you can begin using the open-source programming language. It will make things easy for you to code python and finish a wide range of machine learning tasks. 

 

Topics that are Covered: 

  • Artificial Intelligence Basics.
  • Deep neural networks, Decision trees
  • Logistic Regression
  • Python programming language fundamentals

Conclusion

It sums up a few of the best books that can help the reader acquire knowledge in the advanced level of machine learning. You are limited to the books and can always go for the tutorials and the relevant courses. 

 

You don’t want to lose the trending career option that is a happening thing right these days. The future in this field looks promising and enhances into a very profitable profession. You can always increase your knowledge for an upgrade or begin your career path in machine learning now. After learning your way into this field makes you understand the codes more straightforward to finish the tasks.

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