The applied artificial intelligence workshop.

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Published
Birmingham, UK : Packt Publishing, 2020.
Status
Available Online

Description

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 ...

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Format
Language
English
ISBN
180020373X, 9781800203730

Notes

Description
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 ...
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O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

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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Citations

APA Citation, 7th Edition (style guide)

So, A. (. s., So, W., & Nagy, Z. (2020). The applied artificial intelligence workshop . Packt Publishing.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

So, Anthony (Data scientist), William, So and Zsolt, Nagy. 2020. The Applied Artificial Intelligence Workshop. Birmingham, UK: Packt Publishing.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

So, Anthony (Data scientist), William, So and Zsolt, Nagy. The Applied Artificial Intelligence Workshop Birmingham, UK: Packt Publishing, 2020.

Harvard Citation (style guide)

So, A. (. s., So, W. and Nagy, Z. (2020). The applied artificial intelligence workshop. Birmingham, UK: Packt Publishing.

MLA Citation, 9th Edition (style guide)

So, Anthony (Data scientist),, William So, and Zsolt Nagy. The Applied Artificial Intelligence Workshop Packt Publishing, 2020.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work ID78bcdbcb-82cf-dbc8-4992-6167464b017c-eng
Full titleapplied artificial intelligence workshop
Authorso anthony
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Last Update2025-02-11 03:40:45AM
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5050 |a 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
5058 |a 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
5058 |a 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
5058 |a 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
5058 |a 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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