The applied artificial intelligence workshop.

The applied artificial intelligence workshop.

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Bibliographic Details
Author / Creator:So, Anthony (Data scientist), author.
Imprint:Birmingham, UK : Packt Publishing, 2020.
Description:1 online resource (1 volume) : illustrations
Language:English
Subject:
Artificial intelligence.
Machine learning.
Artificial Intelligence
Machine Learning
Artificial intelligence
Machine learning
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/14141046
Hidden Bibliographic Details
Other authors / contributors:So, William, author.
Nagy, Zsolt, author.
ISBN:180020373X
9781800203730
9781800205819
Notes:Online resource; title from title page (viewed October 22, 2020).
Summary:With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activities Key Features Learn about AI and ML algorithms from the perspective of a seasoned data scientist Get practical experience in ML algorithms, such as regression, tree algorithms, clustering, and more Design neural networks that emulate the human brain Book Description You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You'll then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you'll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you'll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you'll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models. What you will learn Create your first AI game in Python with the minmax algorithm Implement regression techniques to simplify real-world data Experiment with classification techniques to label real-world data Perform predictive analysis in Python using decision trees and random forests Use clustering algorithms to group data without manual support Learn how to use neural networks to process and classify labeled images Who this book is for The Applied Artificial Intelligence Workshop is designed for software developers and data scientists who want to enrich their projects with machine learning. Although you do not need any prior experience in AI, it is recommended that you have knowle ...
Other form:Print version: So, Anthony. Applied Artificial Intelligence Workshop : Start Working with AI Today, to Build Games, Design Decision Trees, and Train Your Own Machine Learning Models. Birmingham : Packt Publishing, Limited, ©2020
Table of Contents:
  • Cover
  • FM
  • Copyright
  • Table of Contents
  • Preface
  • Chapter 1: Introduction to Artificial Intelligence
  • Introduction
  • How Does AI Solve Problems?
  • Diversity of Disciplines in AI
  • Fields and Applications of AI
  • Simulation of Human Behavior
  • Simulating Intelligence
  • the Turing Test
  • What Disciplines Do We Need to Pass the Turing Test?
  • AI Tools and Learning Models
  • Intelligent Agents
  • The Role of Python in AI
  • Why Is Python Dominant in Machine Learning, Data Science, and AI?
  • Anaconda in Python
  • Python Libraries for AI
  • A Brief Introduction to the NumPy Library
  • Exercise 1.01: Matrix Operations Using NumPy
  • Python for Game AI
  • Intelligent Agents in Games
  • Breadth First Search and Depth First Search
  • Breadth First Search
  • Depth First Search (DFS)
  • Exploring the State Space of a Game
  • Estimating the Number of Possible States in a Tic-Tac-Toe Game
  • Exercise 1.02: Creating an AI with Random Behavior for the Tic-Tac-Toe Game
  • Activity 1.01: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game
  • Exercise 1.03: Teaching the Agent to Win
  • Defending the AI against Losses
  • Activity 1.02: Teaching the Agent to Realize Situations When It Defends Against Losses
  • Activity 1.03: Fixing the First and Second Moves of the AI to Make It Invincible
  • Heuristics
  • Uninformed and Informed Searches
  • Creating Heuristics
  • Admissible and Non-Admissible Heuristics
  • Heuristic Evaluation
  • Heuristic 1: Simple Evaluation of the Endgame
  • Heuristic 2: Utility of a Move
  • Exercise 1.04: Tic-Tac-Toe Static Evaluation with a Heuristic Function
  • Using Heuristics for an Informed Search
  • Types of Heuristics
  • Pathfinding with the A* Algorithm
  • Exercise 1.05: Finding the Shortest Path Using BFS
  • Introducing the A* Algorithm
  • A* Search in Practice Using the simpleai Library
  • Game AI with the Minmax Algorithm and Alpha-Beta Pruning
  • Search Algorithms for Turn-Based Multiplayer Games
  • The Minmax Algorithm
  • Optimizing the Minmax Algorithm with Alpha-Beta Pruning
  • DRYing Up the Minmax Algorithm
  • the NegaMax Algorithm
  • Using the EasyAI Library
  • Activity 1.04: Connect Four
  • Summary
  • Chapter 2: An Introductionto Regression
  • Introduction
  • Linear Regression with One Variable
  • Types of Regression
  • Features and Labels
  • Feature Scaling
  • Splitting Data into Training and Testing
  • Fitting a Model on Data with scikit-learn
  • Linear Regression Using NumPy Arrays
  • Fitting a Model Using NumPy Polyfit
  • Plotting the Results in Python
  • Predicting Values with Linear Regression
  • Exercise 2.01: Predicting the Student Capacity of an Elementary School
  • Linear Regression with Multiple Variables
  • Multiple Linear Regression
  • The Process of Linear Regression
  • Importing Data from Data Sources
  • Loading Stock Prices with Yahoo Finance
  • Exercise 2.02: Using Quandl to Load Stock Prices

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