Artificial Intelligence with Python Second Edition

Your complete guide to building intelligent apps using Python 3.x

Alberto Artasanchez , Prateek Joshi 지음

Packt(GCO Science) 2020년 01월 31일 출간

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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.

▶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 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

▶Key Features
-Completely updated and revised to Python 3.x
-New chapters for AI on the cloud, recurrent neural networks, deep learning models, and featu

목차

▶TABLE of CONTENTS
-Chapter 1: Introduction to Artificial Intelligence
-Chapter 2: Fundamental Use Cases for Artificial Intelligence
-Chapter 3: Machine Learning Pipelines
-Chapter 4: Feature Selection and Feature Engineering
-Chapter 5: Classification and Regression Using Supervised Learning
-Chapter 6: Predictive Analytics with Ensemble Learning
-Chapter 7: Detecting Patterns with Unsupervised Learning
-Chapter 8: Building Recommender Systems
-Chapter 9: Logic Programming
-Chapter 10: Heuristic Search Techniques
-Chapter 11: Genetic Algorithms and Genetic Programming
-Chapter 12: Artificial Intelligence on the Cloud
-Chapter 13: Building Games with Artificial Intelligence
-Chapter 14: Building a Speech Recognizer
-Chapter 15: Natural Language Processing
-Chapter 16: Chatbots
-Chapter 17: Sequential Data and Time Series Analysis
-Chapter 18: Image Recognition
-Chapter 19: Neural Networks
-Chapter 20: Deep Learning with Convolutional Neural Networks
-Chapter 21: Recurrent Neural Networks and Other Deep Learning Models
-Chapter 22: Creating Intelligent Agents with Reinforcement Learning
-Chapter 23: Artificial Intelligence and Big Data

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▶What this book covers
- Chapter 1, Introduction to Artificial Intelligence
This chapter provides some basic definitions and groupings that will be used throughout the book. It will also provide an overall classification of the artificial intelligence and machine learning fields as they exist today.

- Chapter 2, Fundamental Use Cases for Artificial Intelligence
Artificial Intelligence is a fascinating topic and a vast field of knowledge. In its current state it generates more questions than it answers, but there are certainly many places where artificial intelligence is being applied, in many instances without us even realizing. Before we delve into the fundamental algorithms that drive AI, we will analyze some of the most popular use cases for the technology as of today.

- Chapter 3, Machine Learning Pipelines
Model training is only a small piece of the machine learning process. Data scientists often spend a significant amount of time cleansing, transforming, and preparing data to get it ready to be consumed by an AI model. Since data preparation is such a time-consuming activity, we will present state-of-the-art techniques to facilitate this activity as well as other components that a well-designed production data pipeline should possess.

- Chapter 4, Feature Selection and Feature Engineering
Model performance can be improved by selecting the right dimensions to pass to the model as well as discovering new dimensions that can enrich the input datasets. This chapter will demonstrate how new features can be created from existing ones as well as from external sources. It will also cover how to eliminate redundant or lowvalue features.

- Chapter 5, Classification and Regression Using Supervised Learning
This chapter defines in detail supervised learning. It provides a taxonomy of the various methods and algorithms for problems that fall under this classification.

- Chapter 6, Predictive Analytics with Ensemble Learning
Ensemble learning is a powerful technique th

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▶ Preface
Recent advances in artificial intelligence (AI) have placed great power into the hands of humans. With great power comes a proportional level of responsibility. Self-driving cars, chatbots, and increasingly accurate predictions of the future are but a few examples of AI's ability to supercharge humankind's capacity for growth and advancement.

AI is becoming a core, transformative path that is changing the way we think about every aspect of our lives. It is impacting industry. It is becoming pervasive and embedded in our everyday lives. Most excitingly, this is a field that is still in its infancy: the AI revolution has only just begun.

As we collect more and more data and tackle that data with better and faster algorithms, we can use AI to build increasingly accurate models and to answer increasingly complex, previously intractable questions.

From this, it will come as no surprise that the ability to work with and fully utilize AI will be a skill that is set only to increase in value. In this book, we explore various real-world scenarios and learn how to apply relevant AI algorithms to a wide swath of problems.

The book starts with the most basic AI concepts and progressively builds on these concepts to solve increasingly difficult problems. It will use the initial knowledge gleaned during the beginning chapters as a foundation to allow the reader to explore and tackle some of the more complicated problems in AI. By the end of the book, the reader will have gained a solid understanding of many AI techniques and will have gained confidence about when to use these techniques.

We will start by talking about various realms of AI. We'll then move on to discuss more complex algorithms, such as extremely random forests, Hidden Markov Models, genetic algorithms, artificial neural networks, convolutional neural networks, and so on.

This book is for Python programmers looking to use AI algorithms to create realworld applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques.

You will learn how to make informed decisions about the type of algorithms you need to use and how to implement those algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on artificial intelligence will definitely come to your rescue!

작가정보

저자(글) Alberto Artasanchez

Alberto Artasanchez is a data scientist with over 25 years of consulting experience with Fortune 500 companies as well as startups. He has an extensive background in artificial intelligence and advanced algorithms. Mr. Artasanchez holds 8 AWS certifications including the Big Data Specialty and the Machine Learning Specialty certifications. He is an AWS Ambassador and publishes frequently in a variety of data science blogs. He is often tapped as a speaker on topics ranging from Data Science, Big Data and Analytics, underwriting optimization and fraud detection. He has a strong and extensive track record designing and building end-to-end machine learning platforms at scale. He graduated with a Master of Science degree from Wayne State University and a Bachelor of Art degree from Kalamazoo College. He is particularly interested in using Artificial Intelligence to build Data Lakes at scale. He is married to his lovely wife Karen and is addicted to CrossFit.

저자(글) Prateek Joshi

Prateek Joshi is the founder of Plutoshift and a published author of 9 books on Artificial Intelligence. He has been featured on Forbes 30 Under 30, NBC, Bloomberg, CNBC, TechCrunch, and The Business Journals. He has been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Silicon Valley Deep Learning. Apart from Artificial Intelligence, some of the topics that excite him are number theory, cryptography, and quantum computing. His greater goal is to make Artificial Intelligence accessible to everyone so that it can impact billions of people around the world.