Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x, 2nd Edition
Produktdetaljer
Biographical note
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. 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.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 engineering
- Learn more about deep learning algorithms, machine learning data pipelines, and chatbots
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
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.
- 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