Introduction to ML.NET

Introduction to ML.NET

Introduction to ML.NET. In this video, I'm going to walk you through a fast and simple example of leveraging ML.NET for detecting anomalies in your system.

In this video, I'm going to walk you through a fast and simple example of leveraging ML.NET for detecting anomalies in your system.
Here's also some resources that you might find useful:
Source code: https://github.com/hassanhabib/ML.NET-DEMO
ML.NET Resources: https://dotnet.microsoft.com/apps/machinelearning-ai/ml-dotnet

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Machine Learning for .NET Developers

Machine Learning for .NET Developers

Machine Learning becomes available and easy for .NET developers. Now you don’t have to learn a new language (such as Python or R) and gain a data scientist degree. This is an introductory talk where I’ll provide some machine learning basics and show how you can easily start using machine learning in your .NET applications via ML.NET and other offerings from Microsoft.

Machine Learning becomes available and easy for .NET developers. Now you don’t have to learn a new language (such as Python or R) and gain a data scientist degree.

This is an introductory talk where I’ll provide some machine learning basics and show how you can easily start using machine learning in your .NET applications via ML.NET and other offerings from Microsoft.

The Complete Guide to Machine Learning with ML.NET

The Complete Guide to Machine Learning with ML.NET

Welcome to Machine Learning with ML.NET! In this introductory video series we will introduce the concept of Machine Learning, what you can do with various tooling, and how to get started with ML.NET!

The Complete Guide to Machine Learning with ML.NET

1. ML.NET - Machine Learning Introduction
Welcome to Machine Learning with ML.NET! In this introductory video series we will introduce the concept of Machine Learning, what you can do with various tooling, and how to get started with ML.NET!

2. ML.NET Introduction
Learn all about what ML.NET is and what you can do with the Framework. You will also see how customers using it, the open source momentum, and a short demo of the experience.

3.Getting started with ML.NET
Learn how you can get started with ML.NET on Windows, Mac, and Linux using tools (ML.NET Model Builder, ML.NET CLI) or code-first using ML.NET API.

4. Build a ML model for Sentiment Analysis
In this video, we will cover how to build a ML model for sentiment analysis of customer reviews using a binary classification algorithm.

5. Build a ML model for GitHub Issue classification
In the world of ML.NET there is a high-volume of interaction with GitHub. This video will teach you how to classify incoming GitHub issues into one of the many tags (labels) using a multi-class classification algorithm.

6. Build a ML model for predicting taxi fares
There are tons of different ways to use the model builder with ML.NET. In this video, we learn how to predict taxi fares based on distance traveled, trip time etc. using a regression algorithm.

7. Build a ML model for Movie Recommendation
Here are even more ways to use the model builder with ML.NET. In this video, we learn how to recommend movies for users using colloborative filtering based recommendation approach.

8. Deep learning with ML.NET: Image Classification
Deep learning enables many more scenarios using sound, images, text and other data types. Learn how to build an Image Classification model to classify flowers (daisies, roses etc.).

Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.

Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03