Not only you, everyone wants to learn data science and machine learning. Both Data Science and Machine Learning are in high demand nowadays. Data scientists and machine learning engineers demand more as more companies use cutting-edge technology.
Machine Learning and Data Science offer numerous opportunities. Buy top-notch books to discover more about this trending platform. Books can help you learn more about machine learning/data science and update you on industry trends.
A data scientist can quickly learn machine learning and data science by taking free online courses. Enroll in the Machine Learning certification course to advance your career as a Machine Learning Engineer. It’s never too late to stay up with the ever-changing world of technology. If you work in Data Science, Machine Learning, or related subjects, here is a list of 11 free books you must read this new year.
Think Stats: Probability and Statistics for Programmers (written by Allen B. Downey)
Think Stats is a Python programmers’ guide to probability and statistics. You may use Think Stats to study real data sets and answer interesting questions. The book includes a case study utilizing NIH data. Readers are invited to create projects using actual data.
Understanding Machine Learning: From Theory to Algorithms (written by Shai Ben-David and Shai Shalev-Shwartz)
Machine Learning is a rapidly expanding field of computer science with broad applications. This book introduces machine learning and its algorithmic concepts and explains the mathematical derivations that turn them into practical algorithms. It contains essential algorithmic paradigms such as stochastic gradient descent (SGD), neural networks (NN), and structured outcome learning (SOL), as well as developing theoretical topics. The book also includes essential subjects not addressed by primary textbooks.
Introducing Data Science (by Davy Cielen, Arno D. B. Meysman, and Mohamed Ali)
Introducing Data Science discusses fundamental data science principles and shows you how to do basic data scientist jobs. You’ll learn about graph databases, NoSQL, and the data science method. You’ll use Python and standard Python libraries to learn about data management at scale. Learn how Python can help you analyze data sets that are too large to fit on a single system or data flowing too fast for a single machine. This book teaches you to use Scikit-learn and StatsModels. This book will give you the foundation you need to begin a career in data science.
Introduction to Probability (by Joseph K. Blitzstein and Jessica Hwang)
Probability comes next after statistics. This book will teach you to the principles using real-life examples. This book builds on the real possibility that you learned in school. Beginners should spend more time with opportunities than experienced ones. Assemble a solid foundation in data science with this book.
Python for Data Analysis (written by Wes McKinney)
Data science requires data analytics. Python is a famous and helpful programming language for Data Analytics and Machine Learning. It is a complete guide for data science novices to learn Data Analytics using Python. The novel is fast-paced but easy. It is well-organized and gives readers a glimpse into the world of data analysts and data scientists and their work. With this book’s help, you can build actual applications in just a week.
Top Programming Languages for a Data Scientist
To gain new abilities or design a mobile app, a candidate must choose the correct programming language to learn. This eBook covers ten of today’s most popular and important programming languages. Students can learn a bit of the language and its difficulty from each example. In addition, they’ll learn how to utilize it and which language to begin with.
Machine Learning: The New AI (by Ethem Alpaydin)
This book, which was just released, goes into great detail regarding the techniques used on various data sets and how programmers may use them to generate new code. As the author of Introduction to Machine Learning, Alpaydin is well-known in the subject.
Python Machine Learning (by Sebestain Raschka)
It is a Python-specific book that focuses on the usage of machine learning with Python. It is one of the best books on machine learning because most experts utilize Python as their programming language. It is one of the best books on machine learning for Python developers. The scikit-learn library and data analysis are also covered in the book.
The Elements of Statistical Learning (by Trevor Hastie, Robert Tibshirani, and Jerome Friedman)
This book is the first to completely address subjects including neural networks, SVMs, classification trees, and reinforcement learning. As presented in this book, a shared conceptual framework unifies the key ideas from each of these fields. Despite the statistical methodology, the focus is on the concepts rather than the mathematicians who implement them. Numerous examples are provided with the use of color charts.
Python Machine Learning: A Technical Approach to Machine Learning for Beginners (by Leonard Eddison)
This book explains machine learning principles and their significance in the digital environment in an approachable manner. Machine learning’s many subfields and applications are also covered in depth in this book. It also provides a thorough introduction to Python’s free and open-source programming language. This machine learning textbook prepares you to write Python code to accomplish many machine learning tasks.
Python Data Science Handbook (by Jake VanderPlas)
Python’s tools for obtaining, cleaning, and deriving valuable insights from data make it an invaluable tool. Many data scientists and machine learning experts use Python to solve the most complex data science challenges. This book is well-suited for people who have already learned the fundamentals of Python and wish to move on to more advanced material.
That summarizes the 11 top machine learning/data science books you can read to advance in the latest field. Besides books, you may learn about machine learning from the top machine learning tutorials, YouTube videos, online courses, and more. Working in machine learning and data science is a popular choice. It has a bright future ahead of it. So, now is the best time to enter the field and turn a profit.
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