Introduction to ML.NET or Machine Learning with .NET
3.5 (22 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,122 students enrolled

Introduction to ML.NET or Machine Learning with .NET

Understand Machine Learning and how to use it with .NET and C# in a real world application using different examples
3.5 (22 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,122 students enrolled
Created by Harshit Gindra
Last updated 8/2019
English
English [Auto]
Current price: $11.99 Original price: $19.99 Discount: 40% off
2 days left at this price!
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This course includes
  • 40 mins on-demand video
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Understand what is Machine Learning
  • Machine Learning basic concepts
  • Machine Learning with .Net technologies
  • ML .Net
  • Create Machine Learning Models
  • Machine Learning Algorithms
  • Basic understanding about Data Science
  • How Machine Learning works with examples
  • Creating machine learning models using .net technologies
Course content
Expand all 11 lectures 39:54
+ Course Overview
1 lecture 01:03

This video contains brief introduction about the Machine Learning, Course Overview, Topics covered in the following videos, different ways of building models using ML.NET. After watching this video, you will get a  clear understanding of the course structure and the topics that are going to be covered in this course.

Preview 01:03
+ Introduction to Machine Learning
2 lectures 05:21

This lecture explains what Machine Learning is. It will help students realize the extensibility of Machine Learning and how we are surrounded by technologies working on Machine Learning. After this course, student will be able to understand the basic definition of Machine Learning and the scope of its application.

Preview 04:48

This lecture talks about what is Machine Learning for .Net framework. It gives a fair introduction about Machine Learning with .NET. After this course, students will be able to understand more about ML.NET and some of its advantages.

Preview 00:33
+ How Machine Learning Works
2 lectures 03:37

This lecture talks about how Machine Learning tries to find pattern in the dataset. It provides an example of the dataset and asks the viewers to identify patterns. It explains that with increasing number of rows and columns, it becomes difficult to identify patterns and make predictions. After this lecture, students will be able to understand how to find pattern in the dataset and how Machine Learning does such complex process for us.

Preview 01:55

This lecture talks about the Machine Learning Process. We need to go through several steps to build and train the model before it is ready to be used in live environment. After this lecture, students will be get a better understanding of how Machine Learning process works and how models are generated in an iterative process.

Preview 01:42
+ ML.NET
5 lectures 29:08

This lecture talks about different ways/options available to generate machine learning models through ML.NET. After this lecture, students will be familiar with the different ways of generating ML models.

Getting started with ML.NET
00:59

This lecture talks about using Visual Studio Extension to build a model. Taking an example dataset, this lecture will walk you through the steps involved in generating a model through the extension. After this lecture, students will be able to take a sample dataset and generate a Machine Learning Model using Visual Studio Extension.

Machine Learning with Model Builder
06:53

This lecture talks about using ML.NET Command Line Interface or CLI to build a model. Taking an example dataset, this lecture will walk you through the steps involved in generating a model through the Terminal. Similar steps can be executed on Command Prompt(Windows) and Bash(Linux). After this lecture, students will be able to take a sample dataset and generate a Machine Learning Model using the CLI.

ML.NET CLI or Command Line Interface
08:55

This lecture talks about using C# and the ML.NET nuget packages or CLI to build a model from scratch. Taking an example dataset, this lecture will walk you through the code involved in generating a model through the C# and Visual Studio.After this lecture, students will be able to take a sample dataset and generate a Machine Learning Model from scratch.

The code developed in the lecture is also available in the lecture materials

ML.NET with C#
09:16
Consuming the Model
03:05
+ Conclusion
1 lecture 00:45

This lecture talks about the current status of ML.NET tech and concludes the course.

Conclusion
00:45
Requirements
  • Basic understanding of C#
  • Basic knowledge of using Visual Studio
Description

This course will help students understand what is Machine Learning, the process involved in Machine Learning and how we can do Machine Learning using .NET technologies or Libraries. This course throws light on some of the Machine Learning concepts, its applications, steps involved in building models and consuming those models using C#, .NET and Visual Studio. This course explains the steps involved in building models using different examples and different algorithms suited for each of the scenarios.

Who this course is for:
  • C# Developers curious about data science
  • Machine Learning aspirants wanting to implement machine learning with .net
  • Web developers interested in getting started with Machine Learning
  • Software engineers interested in getting started with Data Science
  • Software Developers interested in getting started with Data Science
  • Software Developers interested in getting started with Machine Learning
  • Software engineers interested in getting started with Machine Learning
  • Newbies to Machine Learning
  • Newbies to Data Science
  • C# developers interested in Machine Learning
  • Developers interested in understanding predictive algorithms