Artificial Intelligence and Machine Learning Fundamentals : Develop Real-world Applications Powered by the Latest AI Advances

Create AI applications in Python and lay the foundations for your career in data science Key Features Practical examples that explain key machine learning algorithms Explore neural networks in detail with interesting examples Master core AI concepts with engaging activities Book Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learn Understand the importance, principles, and fields of AI Implement basic artificial intelligence concepts with Python Apply regression and classification concepts to real-world problems Perform predictive analysis using decision trees and random forests Carry out clustering using the k-means and mean shift algorithms Understand the fundamentals of deep learning via practical examples Who this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it's recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python) Preface......Page 30 Objectives......Page 32 Minimum Hardware Requirements......Page 34 Software Requirements......Page 35 Installation and Setup......Page 36 Starting Anaconda......Page 38 Additional Resources......Page 41 Principles of Artificial Intelligence......Page 43 Introduction......Page 45 How does AI Solve Real World Problems?......Page 46 Diversity of Disciplines......Page 48 Fields and Applications of Artificial Intelligence......Page 50 Simulating Intelligence – The Turing Test......Page 55 AI Tools and Learning Models......Page 56 Classification and Prediction......Page 57 Learning Models......Page 58 The Role of Python in Artificial Intelligence......Page 59 Why is Python Dominant in Machine Learning, Data Science, and AI?......Page 60 Anaconda in Python......Page 61 Python Libraries for Artificial Intelligence......Page 63 A Brief Introduction to the NumPy Library......Page 65 Exercise 1: Matrix Operations Using NumPy......Page 70 Intelligent Agents in Games......Page 75 Breadth First Search and Depth First Search......Page 77 Exploring the State Space of a Game......Page 85 Exercise 2: Estimating the Number of Possible States in Tic-Tac-Toe Game......Page 92 Exercise 3: Creating an AI Randomly......Page 94 Activity 1: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game......Page 100 Summary......Page 103 AI with Search Techniques and Games......Page 104 Exercise 4: Teaching the Agent to Win......Page 106 Activity 2: Teaching the Agent to Realize Situations When It Defends Against Losses......Page 109 Activity 3: Fixing the First and Second Moves of the AI to Make it Invincible......Page 111 Creating Heuristics......Page 115 Admissible and Non-Admissible Heuristics......Page 117 Heuristic Evaluation......Page 118 Exercise 5: Tic-Tac-Toe Static Evaluation with a Heuristic Function......Page 126 Types of Heuristics......Page 130 Pathfinding with the A* Algorithm......Page 132 Exercise 6: Finding the Shortest Path to Reach a Goal......Page 138 Exercise 7: Finding the Shortest Path Using BFS......Page 139 Introducing the A* Algorithm......Page 143 A* Search in Practice Using the simpleai Library......Page 167 Game AI with the Minmax Algorithm and Alpha-Beta Pruning......Page 173 Search Algorithms for Turn-Based Multiplayer Games......Page 174 The Minmax Algorithm......Page 179 Optimizing the Minmax Algorithm with Alpha-Beta Pruning......Page 196 DRYing up the Minmax Algorithm – The NegaMax Algorithm......Page 205 Using the EasyAI Library......Page 207 Activity 4: Connect Four......Page 211 Summary......Page 214 Regression......Page 215 Introduction......Page 217 Linear Regression with One Variable......Page 218 What Is Regression?......Page 219 Features and Labels......Page 229 Feature Scaling......Page 231 Cross-Validation with Training and Test Data......Page 234 Fitting a Model on Data with scikit-learn......Page 236 Linear Regression Using NumPy Arrays......Page 239 Fitting a Model Using NumPy Polyfit......Page 253 Predicting Values with Linear Regression......Page 270 Activity 5: Predicting Population......Page 273 Multiple Linear Regression......Page 277 The Process of Linear Regression......Page 279 Loading Stock Prices with Yahoo Finance......Page 280 Loading Stock Prices with Quandl......Page 284 Exercise 8: Using Quandl to Load Stock Prices......Page 285 Preparing Data for Prediction......Page 287 Performing and Validating Linear Regression......Page 296 Predicting the Future......Page 301 Polynomial and Support Vector Regression......Page 311 Polynomial Regression with One Variable......Page 312 Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression......Page 314 Polynomial Regression with Multiple Variables......Page 322 Support Vector Regression......Page 325 Support Vector Machines with a 3 Degree Polynomial Kernel......Page 331 Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables......Page 334 Summary......Page 336 Classification......Page 337 Introduction......Page 339 The Fundamentals of Classification......Page 340 Exercise 10: Loading Datasets......Page 342 Data Preprocessing......Page 349 Exercise 11: Pre-Processing Data......Page 355 Minmax Scaling of the Goal Column......Page 359 Identifying Features and Labels......Page 361 Activity 7: Preparing Credit Data for Classification......Page 362 The k-nearest neighbor Classifier......Page 363 Introducing the K-Nearest Neighbor Algorithm......Page 365 Distance Functions......Page 366 Exercise 12: Illustrating the K-nearest Neighbor Classifier Algorithm......Page 372 Exercise 13: k-nearest Neighbor Classification in scikit-learn......Page 379 Exercise 14: Prediction with the k-nearest neighbors classifier......Page 381 Parameterization of the k-nearest neighbor Classifier in scikit-learn......Page 382 Activity 8: Increasing the Accuracy of Credit Scoring......Page 383 Classification with Support Vector Machines......Page 385 What are Support Vector Machine Classifiers?......Page 386 Understanding Support Vector Machines......Page 388 Support Vector Machines in scikit-learn......Page 397 Activity 9: Support Vector Machine Optimization in scikit-learn......Page 399 Summary......Page 402 Using Trees for Predictive Analysis......Page 403 Introduction to Decision Trees......Page 405 Entropy......Page 416 Exercise 15: Calculating the Entropy......Page 421 Information Gain......Page 425 Gini Impurity......Page 427 Exit Condition......Page 433 Building Decision Tree Classifiers using scikit-learn......Page 434 Evaluating the Performance of Classifiers......Page 436 Exercise 16: Precision and Recall......Page 440 Exercise 17: Calculating the F1 Score......Page 442 Exercise 18: Confusion Matrix......Page 448 Activity 10: Car Data Classification......Page 452 Random Forest Classifier......Page 455 Constructing a Random Forest......Page 457 Random Forest Classification Using scikit-learn......Page 459 Feature Importance......Page 461 Extremely Randomized Trees......Page 463 Activity 11: Random Forest Classification for Your Car Rental Company......Page 464 Summary......Page 466 Clustering......Page 467 Introduction to Clustering......Page 469 Defining the Clustering Problem......Page 470 Clustering Approaches......Page 477 Clustering Algorithms Supported by scikit-learn......Page 478 The k-means Algorithm......Page 480 Exercise 19: k-means in scikit-learn......Page 482 Exercise 20: Retrieving the Center Points and the Labels......Page 494 Activity 12: k-means Clustering of Sales Data......Page 496 Exercise 21: Illustrating Mean Shift in 2D......Page 499 Mean Shift Algorithm in scikit-learn......Page 511 Image Processing in Python......Page 517 Activity 13: Shape Recognition with the Mean Shift Algorithm......Page 521 Summary......Page 523 Deep Learning with Neural Networks......Page 524 Introduction......Page 526 TensorFlow for Python......Page 527 Installing TensorFlow in the Anaconda Navigator......Page 528 Exercise 22: Using Basic Operations and TensorFlow constants......Page 530 Placeholders and Variables......Page 533 Global Variables Initializer......Page 534 Introduction to Neural Networks......Page 536 Biases......Page 549 Use Cases for Artificial Neural Networks......Page 552 Activation Functions......Page 554 Exercise 23: Activation Functions......Page 563 Forward and Backward Propagation......Page 567 Importing the TensorFlow Digit Dataset......Page 569 Modeling Features and Labels......Page 571 TensorFlow Modeling for Multiple Labels......Page 575 Optimizing the Variables......Page 577 Training the TensorFlow Model......Page 581 Testing the Model......Page 582 Randomizing the Sample Size......Page 584 Activity 14: Written Digit Detection......Page 585 Adding Layers......Page 588 Convolutional Neural Networks......Page 591 Activity 15: Written Digit Detection with Deep Learning......Page 592 Summary......Page 595 Appendix......Page 597 Activity 1: Generating All Possible Sequences of Steps in the tic-tac-toe Game......Page 599 Activity 2: Teach the agent realize situations when it defends against losses......Page 606 Activity 3: Fix the first and second moves of the AI to make it invincible......Page 610 Activity 4: Connect Four......Page 614 Activity 5: Predicting Population......Page 622 Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables......Page 627 Activity 7: Preparing Credit Data for Classification......Page 638 Activity 8: Increase the accuracy of credit scoring......Page 645 Activity 9: Support Vector Machine Optimization in scikit-learn......Page 646 Activity 10: Car Data Classification......Page 649 Activity 11: Random Forest Classification for your Car Rental Company......Page 655 Activity 12: k-means Clustering of Sales Data......Page 668 Activity 13: Shape Recognition with the Mean Shift algorithm......Page 671 Activity 14: Written digit detection......Page 699 Activity 15 : Written Digit Detection with Deep Learning......Page 706 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.