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Home » Technology » AI / Machine Learning » Machine Learning with ML.NET

Course Skill Level:

Foundational

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    MLMLNEL21E09

Who should attend & recommended skills:

Python developers with basic IT and Linux skills

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others 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.
  • Foundation-level Machine Learning with ML.NETskills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning with ML.NET expert instructor, students will learn about and explore:
  • Getting well-versed with the ML.NET framework and its components and APIs using practical examples
  • 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
  • The framework, components, and APIs of ML.NET using C#
  • Developing regression models using ML.NET for employee attrition and file classification
  • Evaluating classification models for sentiment prediction of restaurant reviews
  • Working with clustering models for file type classifications
  • Using anomaly detection to find anomalies in both network traffic and login history
  • Working with ASP.NET Core Blazor to create an ML.NET enabled web application
  • Integrating pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detection

Course breakdown / modules

  • 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?

  • Setting up your development environment
  • Creating your first ML.NET application
  • Evaluating the model

  • Breaking down regression models
  • Creating the linear regression application
  • Creating the logistic regression application
  • Evaluating a regression model

  • Breaking down classification models
  • Creating a binary classification application
  • Creating a multi-class classification application
  • Evaluating a classification model

  • Breaking down the k-means algorithm
  • Creating the clustering application
  • Evaluating a k-means model

  • Breaking down anomaly detection
  • Creating a time series application
  • Creating an anomaly detection application
  • Evaluating a randomized PCA model

  • Breaking down matrix factorizations
  • Creating a matrix factorization application
  • Evaluating a matrix factorization model

  • Breaking down the .NET Core application architecture
  • Creating the stock price estimator application
  • Exploring additional production application enhancements

  • Breaking down ASP.NET Core
  • Creating the file classification web application
  • Exploring additional ideas for improvements

  • Breaking down the UWP architecture
  • Creating the web browser classification application
  • Additional ideas for improvements

  • Investigating feature engineering
  • Obtaining training and testing datasets
  • Creating your model-building pipeline

  • Breaking down ONNX and YOLO
  • Creating the ONNX object detection application
  • Exploring additional production application enhancements

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