Data Science Books for Beginners and Experts: Top Picks
This is a great book for learning about probability as it gives clear explanations that mirror real-life situations. It delves into a wide range of applications and case studies.
The book will show you practical techniques for developing your own machine-learning solutions using Python. Learn to build an ML application using Python and Scikit-Learn library.
Featuring a realistic, up-to-date introduction to Python data science tools, this book explains how to manipulate, analyze, clean, and crunch datasets using Python.
It introduces approximate inference methods for quick approximate answers when exact solutions aren't possible. Graphical models are used to characterize probability distributions.
This book teaches you how exploratory data analysis is one of the first steps in data science, as well as how random sampling can eliminate bias and produce better datasets.
A textbook that discusses linear algebra, probability, information theory, numerical computations, ML, and more. Deep learning, recurrent neural networks, etc., are also covered.
The book illustrates how to mine data that arrive too quickly for exhaustive processing using locality-sensitive hashing and stream-processing methods.