Introduction:

This training program is designed to equip participants with the skills to integrate machine learning into C# applications using ML.NET. It empowers them to build, train, and deploy machine learning models within .NET applications, leveraging C# for creating intelligent solutions.

Program Objectives:

By the end of this program, participants will be able to:

Understand the basics of C# and its role in machine learning.
Build machine learning models using ML.NET.
Integrate trained models into C# applications.
Apply machine learning techniques for classification, regression, and clustering tasks.
Deploy machine learning models in real-world applications.

Target Audience:

C# Developers interested in machine learning.
Software Engineers and Data Scientists.
IT professionals looking to integrate machine learning into their applications.
Developers seeking to expand their knowledge of ML.NET.

Program Outline:

Unit 1:

Introduction to C# and ML.NET:

Overview of C# and its role in application development.
Introduction to ML.NET: An open-source machine learning framework for .NET.
Setting up the development environment for C# and ML.NET.
Basics of machine learning concepts: supervised and unsupervised learning.
How to create a simple ML.NET application with C#.

Unit 2:

Data Processing and Preparation:

Understanding data structures in C# for machine learning.
Loading, cleaning, and transforming data for machine learning models.
Working with datasets using ML.NET's IDataView.
Feature extraction and selection in ML.NET.
Splitting data into training and testing sets for model development.

Unit 3:

Building and Training Machine Learning Models:

Creating and training classification models in ML.NET (e.g., binary classification).
Developing regression models for prediction tasks.
Implementing clustering models for data grouping.
Training models using ML.NET APIs (MLContext, LoadFromTextFile, TrainTestSplit).

Unit 4:

Evaluating and Deploying ML.NET Models:

Evaluating model accuracy and performance using ML.NET metrics (accuracy, precision, recall).
Cross-validation techniques for improving model performance.
Integrating trained models into C# applications.
Exporting and loading models for production use.
Best practices for deploying machine learning models in .NET environments.

Unit 5:

Advanced Topics in ML.NET and C# Integration:

Using deep learning and neural networks with ML.NET.
Implementing time series forecasting with ML.NET.
Utilizing ML.NET AutoML for automated model selection and tuning.
Scaling machine learning applications using cloud services and .NET.
Optimizing performance and efficiency in C# and ML.NET applications.

Integrating Machine Learning with C# and Mastering ML.NET