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Book details

Format: pdf

File Size: 15 MB

Print Length: 618 pages

Publisher: Packt Publishing; 2 edition (31 January 2020)

Sold by: Amazon Asia-Pacific Holdings Private Limited

Language: English

ASIN: B0842P7KTM



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 are mandatory.



About the Book

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, chatbots, and more.



Key Features



Completely updated and revised to Python 3.x, and TensorFlow 2

Seven new chapters that include AI on the cloud, RNNs and DL models, feature engineering, the machine learning data pipeline, and more

New author with 25 years of experience in artificial intelligence across multiple industries and enterprise domains

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 and TensorFlow 2. 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

Understand RNNs and various DL models

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