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Artificial Intelligence and Machine Learning Fundamentals : Develop Real-World Applications Powered by the Latest AI Advances.
Artificial Intelligence and Machine Learning Fundamentals teaches you machine learning and neural networks from the ground up using real-world examples. After you complete this book, you will be excited to revamp your current projects or build new intelligent networks.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Intro; Preface; Principles of Artificial Intelligence; Introduction; How does AI Solve Real World Problems?; Diversity of Disciplines; Fields and Applications of Artificial Intelligence; Simulating Intelligence
- The Turing Test; AI Tools and Learning Models; Classification and Prediction; Learning Models; The Role of Python in Artificial Intelligence; Why is Python Dominant in Machine Learning, Data Science, and AI?; Anaconda in Python; Python Libraries for Artificial Intelligence; A Brief Introduction to the NumPy Library; Exercise 1: Matrix Operations Using NumPy; Python for Game AI
- Intelligent Agents in GamesBreadth First Search and Depth First Search; Exploring the State Space of a Game; Exercise 2: Estimating the Number of Possible States in Tic-Tac-Toe Game; Exercise 3: Creating an AI Randomly; Activity 1: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game; Summary; AI with Search Techniques and Games; Introduction; Exercise 4: Teaching the Agent to Win; Activity 2: Teaching the Agent to Realize Situations When It Defends Against Losses; Activity 3: Fixing the First and Second Moves of the AI to Make it Invincible; Heuristics
- Uninformed and Informed SearchCreating Heuristics; Admissible and Non-Admissible Heuristics; Heuristic Evaluation; Exercise 5: Tic-Tac-Toe Static Evaluation with a Heuristic Function; Using Heuristics for an Informed Search; Types of Heuristics; Pathfinding with the A* Algorithm; Exercise 6: Finding the Shortest Path to Reach a Goal; Exercise 7: 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 PruningDRYing up the Minmax Algorithm
- The NegaMax Algorithm; Using the EasyAI Library; Activity 4: Connect Four; Summary; Regression; Introduction; Linear Regression with One Variable; What Is Regression?; Features and Labels; Feature Scaling; Cross-Validation with Training and Test Data; Fitting a Model on Data with scikit-learn; Linear Regression Using NumPy Arrays; Fitting a Model Using NumPy Polyfit; Predicting Values with Linear Regression; Activity 5: Predicting Population; Linear Regression with Multiple Variables
- Multiple Linear RegressionThe Process of Linear Regression; Importing Data from Data Sources; Loading Stock Prices with Yahoo Finance; Loading Files with pandas; Loading Stock Prices with Quandl; Exercise 8: Using Quandl to Load Stock Prices; Preparing Data for Prediction; Performing and Validating Linear Regression; Predicting the Future; Polynomial and Support Vector Regression; Polynomial Regression with One Variable; Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression; Polynomial Regression with Multiple Variables; Support Vector Regression