Dr. Ameet Joshi received his PhD from Michigan State University in 2006. He has over 15 years of experience in developing machine learning algorithms in various different industrial settings including Pipeline Inspection, Home Energy Disaggregation, Microsoft Cortana Intelligence and Business Intelligence in CRM. He is currently a Data Science Product Manager at Microsoft. Previously, he has worked as Machine Learning Specialist at Belkin International and a Director of Research at Microline Technology Corp. He is a member of several technical committees, has published in numerous conference and journal publications and contributed to edited books. He also has two patents and have received several industry awards including and Senior Membership of IEEE.

Presents a full reference to artificial intelligence and machine learning techniques - in theory and application

Connects all ML and AI techniques to applications and provides their implementations

Includes exercises to augment the concepts discussed from the chapters to solidify the learnings

Introduction.- Introduction to AI and ML.- Essential Concepts in Artificial Intelligence and Machine Learning.- Data Understanding, Representation, and Visualization.- Linear Methods.- Perceptron and Neural Networks.- Decision Trees.- Support Vector Machines.- Probabilistic Models.- Dynamic Programming and Reinforcement Learning.- Evolutionary Algorithms.- Time Series Models.- Deep Learning.- Emerging Trends in Machine Learning.- Unsupervised Learning.- Featurization.- Designing and Tuning.- Model Pipelines.- Performance Measurement.- Classification.- Regression.- Ranking.- Recommendations Systems.- Azure Machine Learning.- Open Source Machine Learning Libraries.- Amazon's Machine Learning Toolkit: Sagemaker.- Conclusion.