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.
2. Artificial Intelligence- A Modern Approach (3rd Edition)
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.
3. Artificial Intelligence By Example - 2nd Edition
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 and how random sampling can eliminate bias and produce better datasets.
5. Artificial Intelligence and Machine Learning-1st Edition
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.
In this book, you will learn how artificial intelligence is revolutionizing the healthcare industry and about current and future AI applications in healthcare.
Learn how to document SQL statements using ANSI standard SQL statements in this book. Data types, grouping and set operations, and data scaling, are just a few of the topics covered.