Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, …
The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX!Deep Learning with Python, Third Edition puts the power of deep learning in your hands.This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX
Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner's guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as…
In this exciting, innovative new textbook, you’ll learn hands-on with today’s most compelling, leading-edge computing technologies—and, as you’ll see, with an easily tunable mix of computer science and data science appropriate for introductory courses in those and related disciplines. Included Index
The book Deep Learning with Python (2nd ed.) is about machine learning, neural networks, and programming with Python
This book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Index : page 811-834.
This book tells a practical guide to using Python for data science and analysis. Index : page 551-563.