6 Data Science eBooks for $15

Download 6 Data Science Books for $15

eBook 1:

Practical Linear Algebra for Data Science (1st ed)

This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms.

eBook 2:

Deep Learning for Coders with fastai and PyTorch (1st ed)

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.

eBook 3:

Hands-On Large Language Models (1st ed)

AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems can write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book's visually educational nature, readers will learn practical tools and concepts they need to use these capabilities today.

eBook 4:

Data Science from Scratch (2nd ed)

To learn data science, you should not only master the tools―data science libraries, frameworks, modules, and toolkits―but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.

eBook 5:

Essential Math for Data Science (1st ed)

Master the math needed to excel in data science, machine learning, and statistics. In this book, author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics, and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way, you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.

eBook 6:

Python Data Science Handbook (2nd ed)

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.