Integrating Machine Learning with C# and Mastering ML.NET

Overview:

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.