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
- New chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineering
- 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 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
- 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
Alberto Artasanchez is a solutions architect with expertise in the cloud, data solutions, and machine learning. His career spans 28+ years in various industries. He is an AWS Ambassador and frequently publishes in various cloud and data science publications. Alberto is often tapped as a speaker on topics such as data science, big data, and analytics. He has a strong and extensive track record of designing and building end-to-end machine learning platforms at scale. He also has a long track record of leading data engineering teams. He has a great understanding of how technology drives business value and has a passion for creating elegant solutions to complicated problems.
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.
