This book uses both best of breed open source software and the author's own libraries to introduce the reader to Artificial Intelligence (AI) technologies like genetic algorithms, neural networks, expert systems, machine learning, and statistical natural language processing (NLP)

Artificial intelligence does the seemingly impossible, magically bringing machines to life--driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.

This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.

Bring a new degree of interconnectivity to your world by building your own intelligent robots.

The Revenue Acceleration Rules is a unique guide in the business-to-business space, providing a clear framework for more effective marketing in an accounts-based environment.

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on.

Tóm tắt nội dung: Introduction to artificial intelligence; Classification and regression using supervised learning; predictive analytics with ensemble learning; detecting patterns with unsupervised learning; building recommender systems; logic programming; heuristic search techniques; genetic algorithms; building games with artificial intelligence; natural language processing; probabilistic reasoning for sequential data; building a speech recognizer; object detection and tracking; artificial neural networks; reingforcement learning; deep learning with convolutional neural networks.