• Title AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python
• Author(s) Hadelin de Ponteves
• Publisher: Packt Publishing (November 29, 2019); eBook (Free Edition)
• Permission: Free eBook by the Publisher (Packt)
• Hardcover/Paperback 360 pages
• eBook HTML
• Language: English
• ISBN-10: 1838645357
• ISBN-13: 978-1838645359
• Share This:  

Book Description

Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch.

This book teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination.

If you want to add AI to your skillset, this book is for you. It doesn't require data science or machine learning knowledge. Just maths basics (high school level).

• Master the basics of AI without any previous experience
• Build fun projects, including a virtual-self-driving car and a robot warehouse worker
• Use AI to solve real-world business problems
• Learn how to code in Python
• Discover the 5 principles of reinforcement learning
• Create your own AI toolkit

• Hadelin de Ponteves is the co-founder and CEO at BlueLife AI, which leverages the power of cutting-edge Artificial Intelligence to empower businesses to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. Hadelin is also an online entrepreneur who has created 50+ top-rated educational e-courses on topics such as machine learning, deep learning, artificial intelligence, and blockchain, which have reached over 700,000 subscribers in 204 countries.

• Amazon

• Artificial Intelligence
• Machine Learning
• Python Programming
• Neural Networks and Deep Learning

• AI Crash Course (Hadelin de Ponteves)
• The Mirror Site (1) - PDF

• Python Machine Learning Projects (Brian Boucheron, et al)
• Deep Learning with Python (Francois Chollet)
• Foundations of Machine Learning (Mehryar Mohri, et al)

• Deep Learning with PyTorch (Eli Stevens, et al.)

  This book teaches you to create deep learning and neural network systems with PyTorch. It gets you to work right away building a tumor image classifier from scratch. You'll learn best practices for the entire deep learning pipeline, tackling advanced projects.

• Deep Learning with Python, 2nd Edition (Francois Chollet)

  This book introduces the field of deep learning using Python and the powerful Keras library. It offers insights for both novice and experienced machine learning practitioners, and builds your understanding through intuitive explanations and practical examples.

• Dive into Deep Learning (Aston Zhang, et al.)

  This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

• Deep Learning for Coders with Fastai and PyTorch

  This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

• Python Machine Learning Projects (Brian Boucheron, et al)

  This book tries to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning. If you know some Python and you want to use machine learning and deep learning, pick up this book.

• O'Reilly® Python Data Science Handbook: Essential Tools

  Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

• Foundations of Machine Learning (Mehryar Mohri, et al)

  This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

• The Hundred-Page Machine Learning Book (Andriy Burkov)

  Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.