In this lab will discuss on following topics using .NET and C# technologies for understanding Machine Learning -

Chapter 1 - Defining Artificial intelligence and Machine Learning.

Chapter 2 - Fundamentals of Training data, Features, Label, Alogrithm and Model.

Chapter 3 - Creating the feedback predictive system.

Chapter 4 - pre-requisites for doing ML.NET.

Chapter 5 - Defining the class structure of training data.

Chapter 6 - Creating feedback system training data.

Chapter 7 - ML.NET and IDataView.

Chapter 8 - Transforms, FeatureText and Vectors.

Chapter 9 - ML.NET Pipeline.

Chapter 10 - Choosing the Algorithm - Binary classification.

Chapter 11 - Creating the final MODEL.

Chapter 12 - Testing Accuracy of the MODEL.

Chapter 13 - Using the MODEL.

Chapter 14 - Revisiting the full flow of ML.NET process.