Artificial Intelligence with Python – Second Edition by Alberto Artasanchez, Prateek Joshi

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