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Table of Contents
Introduction
Part I Introduction to AI and ML
Essential concepts in AL and ML
Part II Techniques for Static Machine Learning Models
Perceptron and Neural Networks
Decision Trees
Advanced Decision Trees
Support Vector Machines
Probabilistic Models
Deep Learning
Part III Techniques for Dynamic Machine Learning Models
Autoregressive and Moving Average Models
Hidden Markov Models and Conditional Random Fields
Recurrent Neural Networks
Part IV Applications
Classification Regression
Ranking
Clustering
Recommendations
Next Best Actions
Designing ML Pipelines
Using ML Libraries
Azure Machine Learning Studio
Conclusions.
Part I Introduction to AI and ML
Essential concepts in AL and ML
Part II Techniques for Static Machine Learning Models
Perceptron and Neural Networks
Decision Trees
Advanced Decision Trees
Support Vector Machines
Probabilistic Models
Deep Learning
Part III Techniques for Dynamic Machine Learning Models
Autoregressive and Moving Average Models
Hidden Markov Models and Conditional Random Fields
Recurrent Neural Networks
Part IV Applications
Classification Regression
Ranking
Clustering
Recommendations
Next Best Actions
Designing ML Pipelines
Using ML Libraries
Azure Machine Learning Studio
Conclusions.