English | 2020 | ISBN: 183921953X | 619 pages | True PDF EPUB MOBI | 81.58 MB

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more.

Key Features

Completely updated and revised to Python 3.x

New chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and eeering

Learn more about deep learning algorithms, machine learning data pipelines, and chatbots

Book Description

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 eeering; 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 eeering

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 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 Eeering

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 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