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Machine Learning – Understanding and Introduction

Machine learning(ML) is the process of training machines by feeding data to an algorithm. As a result, machines can make smart decisions or good predictions. This prediction can be improved by experience and data.
ML is a branch of artificial intelligence (AI). This enables the ability of computers to learn without being explicitly programmed. So, that machine can carry out certain tasks.

For example, email filtering when we receive any message it gets categories. The machine decides whether an email should go to spam or inbox. There are many other examples like google search prediction Or social media post suggestion or spelling correction, etc.

“Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience”

Tom M. Mitchell

In simple terms, we can say that ML is a process or field for building algorithms. These algorithms can generate intelligence based on data and patterns.

But Why we need machine learning?

A stock-broker knows when to buy or sell which stocks based on his experience in the past. To choose stocks he performs some checks like company performance, insights of company, market condition, past performance of share, etc.
But suppose we have a machine program which performs this check for you. It predicts that stocks. great right!

Machines can carry out all the tasks without human can do. Along with these machines are extra abilities which normal human begins are not capable of like:

  • Analyzing tons and tons of data at a single time.
  • Non-tiring performance
  • Patience
  • Efficient memory access.

The process where a machine becomes capable of the major quality that separates living beings from non-living beings is to learn from experiences, is called ML.

if you see google lens it predicts vai image, handwrite, or logo prediction. It will tell you the object you are looking at or review of the restaurant etc.

Have you ever wonder how these things are working? also, certain apps predict how you look like in your 80’s or 90’s? how is your face going to get some deformations? this is all based upon machine learning.

How to start machine learning?

Machine learning is quite dependent on math. But not all the time it is required but the base core setup of ML depends on Math. There are various options to start with ML like Python, Java, etc. but as a .net developer, I will talk about ML.NET.

ML.NET is a free, open-source, and cross-platform machine learning framework for the .NET developer platform. tutorial, c#, examples, tutorial for beginners, for beginners, step by step

ML.NET allows you to train, build, and ship custom machine learning models using C# or F# for a variety of ML scenarios. ML.NET includes features like automated machine learning (AutoML) and tools like ML.NET CLI and ML.NET Model Builder, which make integrating machine learning into your applications even easier.

The next post will start learning the ML.NET step by step so, stay connected. Prerequisite to learn ML.Net is C# language and understanding about the Development environment.

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