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Machine Learning with ML.NET

  • Course Code: Artificial Intelligence - Machine Learning with ML.NET
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 4 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning with ML.NETskills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending  to Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this course, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The course starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The course will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each lesson will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this course, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET. 

Working in a hands-on learning environment, led by our Machine Learning with ML.NET expert instructor, students will learn about and explore: 

  • Get well-versed with the ML.NET framework and its components and APIs using practical examples 
  • Learn how to build, train, and evaluate popular machine learning algorithms with ML.NET offerings 
  • Extend your existing machine learning models by integrating with TensorFlow and other libraries 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand the framework, components, and APIs of ML.NET using C# 
  • Develop regression models using ML.NET for employee attrition and file classification 
  • Evaluate classification models for sentiment prediction of restaurant reviews 
  • Work with clustering models for file type classifications 
  • Use anomaly detection to find anomalies in both network traffic and login history 
  • Work with ASP.NET Core Blazor to create an ML.NET enabled web application 
  • Integrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detection 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Section 1: Fundamentals of Machine Learning and ML.NET 
  • Section 1: Fundamentals of Machine Learning and ML.NET 
  1. Getting Started with Machine Learning and ML.NET 
  • Getting Started with Machine Learning and ML.NET 
  • The importance of learning about machine learning today 
  • The model building process 
  • Exploring types of learning 
  • Exploring various machine learning algorithms 
  • What is ML.NET? 
  1. Setting Up the ML.NET Environment 
  • Setting Up the ML.NET Environment 
  • Setting up your development environment 
  • Creating your first ML.NET application 
  • Evaluating the model 
  1. Section 2: ML.NET Models 
  • Section 2: ML.NET Models 
  1. Regression Model 
  • Regression Model 
  • Breaking down regression models 
  • Creating the linear regression application 
  • Creating the logistic regression application 
  • Evaluating a regression model 
  1. Classification Model 
  • Classification Model 
  • Breaking down classification models 
  • Creating a binary classification application 
  • Creating a multi-class classification application 
  • Evaluating a classification model 
  1. Clustering Model 
  • Clustering Model 
  • Breaking down the k-means algorithm 
  • Creating the clustering application 
  • Evaluating a k-means model 
  1. Anomaly Detection Model 
  • Anomaly Detection Model 
  • Breaking down anomaly detection 
  • Creating a time series application 
  • Creating an anomaly detection application 
  • Evaluating a randomized PCA model 
  1. Matrix Factorization Model 
  • Matrix Factorization Model 
  • Breaking down matrix factorizations 
  • Creating a matrix factorization application 
  • Evaluating a matrix factorization model 
  1. Section 3: Real-World Integrations with ML.NET 
  • Section 3: Real-World Integrations with ML.NET 
  1. Using ML.NET with .NET Core and Forecasting 
  • Using ML.NET with .NET Core and Forecasting 
  • Breaking down the .NET Core application architecture 
  • Creating the stock price estimator application 
  • Exploring additional production application enhancements 
  1. Using ML.NET with ASP.NET Core 
  • Using ML.NET with ASP.NET Core 
  • Breaking down ASP.NET Core 
  • Creating the file classification web application 
  • Exploring additional ideas for improvements 
  1. Using ML.NET with UWP 
  • Using ML.NET with UWP 
  • Breaking down the UWP architecture 
  • Creating the web browser classification application 
  • Additional ideas for improvements 
  1. Section 4: Extending ML.NET 
  • Section 4: Extending ML.NET 
  1. Training and Building Production Models 
  • Training and Building Production Models 
  • Investigating feature engineering 
  • Obtaining training and testing datasets 
  • Creating your model-building pipeline 
  1. Using TensorFlow with ML.NET 
  1. Using ONNX with ML.NET 
  • Using ONNX with ML.NET 
  • Breaking down ONNX and YOLO 
  • Creating the ONNX object detection application 
  • Exploring additional production application enhancements 
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