New Adaptation of the bestselling information to Synthetic intelligence with Python, up to date to Python 3.x and TensorFlow 2, with seven new chapters that quilt RNNs, AI & Massive Knowledge, basic use Instances, chatbots, and extra.

Key Features

  • Completely up to date and revised to Python 3.x and TensorFlow 2
  • New chapters for AI at the cloud, recurrent neural networks, deep Studying fashions, and have Variety and engineering
  • Learn extra approximately deep Studying algorithms, system Studying Knowledge pipelines, and chatbots

Book Description

Artificial Intelligence with Python, 2nd Adaptation is an up to date and accelerated model of the bestselling information to Synthetic intelligence The usage of the recent model of Python 3.x and TensorFlow 2. Now not best does it supply you an advent to Synthetic intelligence, this new Adaptation is going additional through providing you with the gear you wish to have to discover the superb global of Wise apps and create your personal packages.

This Adaptation additionally comprises seven new chapters on extra complicated ideas of Synthetic Intelligence, together with basic use Instances of AI; system Studying Knowledge pipelines; Function Variety and have engineering; AI at the cloud; the fundamentals of chatbots; RNNs and DL fashions; and AI and Massive Knowledge.

Finally, this new Adaptation explores quite a lot of actual-global situations and teaches you tips on how to observe related AI algorithms to a large swath of issues, beginning with probably the most Elementary AI ideas and steadily construction from there to unravel harder demanding situations in order that through the top, you are going to have received an exceptional figuring out of, and while absolute best to make use of, those many manmade intelligence ways.

What you are going to learn

  • Understand what Synthetic intelligence, system Studying, and knowledge technology are
  • Explore the most typical Synthetic intelligence use Instances
  • Learn tips on how to construct a system Studying pipeline
  • Assimilate the fundamentals of Function Variety and have engineering
  • Identify the diversities among supervised and unsupervised Studying
  • Discover probably the most contemporary advances and gear introduced for AI construction within the cloud
  • Develop automated speech popularity methods and chatbots
  • Apply AI algorithms to time Collection data

Who this guide is for

The meant target audience for this guide is Python builders who need to construct actual-global Synthetic Intelligence packages. Elementary Python programming enjoy and consciousness of system Studying ideas and strategies is necessary.

Table of Contents

  1. Introduction to Synthetic Intelligence
  2. Fundamental Use Instances for Synthetic Intelligence
  3. Machine Studying Pipelines
  4. Feature Variety and Function Engineering
  5. Classification and Regression The usage of Supervised Learning
  6. Predictive Analytics with Ensemble Learning
  7. Detecting Styles with Unsupervised Learning
  8. Building Recommender Systems
  9. Logic Programming
  10. Heuristic Seek Techniques
  11. Genetic Algorithms and Genetic Programming
  12. Artificial Intelligence at the Cloud
  13. Building Video games with Synthetic Intelligence
  14. Building a Speech Recognizer
  15. Natural Language Processing
  16. Chatbots
  17. Sequential Knowledge and Time Collection Analysis
  18. Image Recognition
  19. Neural Networks
  20. Deep Studying with Convolutional Neural Networks
  21. Recurrent Neural Networks and Different Deep Studying Models
  22. Creating Wise Retailers with Reinforcement Learning
  23. Artificial Intelligence and Massive Data