Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learn Understand what artificial intelligence, machine learning, and data science are Explore the most common artificial intelligence use cases Learn how to build a machine learning pipeline Assimilate the basics of feature selection and feature engineering Identify the differences between supervised and unsupervised learning Discover the most recent advances and tools offered for AI development in the cloud Develop automatic speech recognition systems and chatbots Apply AI algorithms to time series data Who this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory. Table of Contents Introduction to Artificial Intelligence Fundamental Use Cases for Artificial Intelligence Machine Learning Pipelines Feature Selection and Feature Engineering 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 and Genetic Programming Artificial Intelligence on the Cloud Building Games with Artificial Intelligence Building a Speech Recognizer Natural Language Processing Chatbots Sequential Data and Time Series Analysis Image Recognition Neural Networks Deep Learning with Convolutional Neural Networks Recurrent Neural Networks and Other Deep Learning Models Creating Intelligent Agents with Reinforcement Learning Artificial Intelligence and Big Data