ML.NET - Machine Learning with .NET Core - Beginner's Guide

ML.NET - Machine Learning with .NET Core - Beginner's Guide

This article will get you started with the fundamentals of Machine Learning and how to get started with Machine Learning with .NET Core i.e. ML.NET. We will even learn different concepts of Machine learning with a brief overview.

This article will get you started with the fundamentals of Machine Learning and how to get started with Machine Learning with .NET Core i.e. ML.NET. We will even learn different concepts of Machine learning with a brief overview.

Table of Contents

Introduction to Machine Learning

Today’s world is full of data and it is increasing day by day. There is a never-ending list of videos to watch, images to view, music to listen to, restaurants to visit, articles to read, stock market data, and we as humans are generating lots of data by the choices we make i.e. videos we watch, the music we listen to, restaurants we visit, etc.

Machine learning is all about computer algorithms that can make some sense out of the data available in the world today. Machine Learning algorithms improve through the experience without any code changes. For computers, this experience is in the form of data. Experience is built by feeding all this data to the algorithm and this data allows the algorithm to learn and build a model. This data which is fed to algorithms for learning is also known as Traning Data. Based on Training data the Machine Learning algorithm builds a model that allows it to make predictions or decisions without being explicitly programmed to do so.

Machine learning algorithm tries to identify the patterns in the data provided and based on this pattern tries to build a model. Once the model is built then additional data, which was not part of training data, is fed to the model to understand the efficiency and correctness of the model built.

Machine Learning is used widely in a variety of application such as

  • Email Filtering
  • Fraud Detection
  • Self Driving Cars
  • Image & Voice Recognition (Siri, Alexa, etc)
  • Stock Market Predictions
  • Recommendations from youtube, Netflix, etc

All entities have been collecting data about you and have been using Machine Learning algorithms to understand your choices so that they can make future suggestions to you based on your choices.

Types of Machine Learning

There are many types of Machine Learning algorithms that you can come across as a practitioner. What will be covering here is traditionally recognized 3 major categories i.e. Supervised, Unsupervised & Reinforcement.

Supervised Learning

As the name suggests it means that the activity will be monitored i.e. observe progress and direct the execution based on the observations. So how do we supervise a Machine Learning model we do so by providing the data to the algorithm which is labeled. For e.g., we provide pictures of cats & dogs to an algorithm to read and build a model to identify whether it’s a cat or a dog but here we provide labeled data i.e. we provide pictures with classification i.e. whether it is a picture of a cat or a dog.

Here the data is labeled to tell the Machine Learning algorithm what patterns it should look for in a cat or in a dog. Once the model is formed with labeled training data new picture of cats and dogs can be fed to the model to understand the correctness of the model.

Unsupervised Learning

In unsupervised learning, data is not labeled i.e. model is allowed to discover patterns on its own. Machine Learning algorithm has to on its own look for patterns and based on these patterns group/classify data provided. Unsupervised learning algorithms are more complex as compared to supervised learning algorithms as there is no information in the data provided and so the algorithm is also not aware of the outcome to be provided.

Unsupervised learning can be used to identify patterns in data which is not known to humans as well. Also, it is easier to get unlabeled data as compared to labeled data as labeling data involved extra work.

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