Machine Learning and Artificial Intelligence

Description

This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application;Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible;Connects all ML and AI techniques to applications and introduces implementations. Front Matter ....Pages i-xxii Front Matter ....Pages 1-1 Introduction to AI and ML (Ameet V Joshi)....Pages 3-7 Essential Concepts in Artificial Intelligence and Machine Learning (Ameet V Joshi)....Pages 9-20 Data Understanding, Representation, and Visualization (Ameet V Joshi)....Pages 21-29 Front Matter ....Pages 31-31 Linear Methods (Ameet V Joshi)....Pages 33-41 Perceptron and Neural Networks (Ameet V Joshi)....Pages 43-51 Decision Trees (Ameet V Joshi)....Pages 53-63 Support Vector Machines (Ameet V Joshi)....Pages 65-71 Probabilistic Models (Ameet V Joshi)....Pages 73-89 Dynamic Programming and Reinforcement Learning (Ameet V Joshi)....Pages 91-98 Evolutionary Algorithms (Ameet V Joshi)....Pages 99-106 Time Series Models (Ameet V Joshi)....Pages 107-115 Deep Learning (Ameet V Joshi)....Pages 117-126 Emerging Trends in Machine Learning (Ameet V Joshi)....Pages 127-132 Unsupervised Learning (Ameet V Joshi)....Pages 133-140 Front Matter ....Pages 141-141 Featurization (Ameet V Joshi)....Pages 143-158 Designing and Tuning Model Pipelines (Ameet V Joshi)....Pages 159-167 Performance Measurement (Ameet V Joshi)....Pages 169-176 Front Matter ....Pages 177-177 Classification (Ameet V Joshi)....Pages 179-184 Regression (Ameet V Joshi)....Pages 185-191 Ranking (Ameet V Joshi)....Pages 193-198 Recommendations Systems (Ameet V Joshi)....Pages 199-204 Front Matter ....Pages 205-205 Azure Machine Learning (Ameet V Joshi)....Pages 207-220 Open Source Machine Learning Libraries (Ameet V Joshi)....Pages 221-232 Amazon’s Machine Learning Toolkit: Sagemaker (Ameet V Joshi)....Pages 233-243 Front Matter ....Pages 245-245 Conclusion and Next Steps (Ameet V Joshi)....Pages 247-248 Back Matter ....Pages 249-261