Brief Introduction to Machine Learning for Beginners

#machinelearning #tutorial #beginners #python

Machine Learning

Machine Learning is the ability of Machines, that is, computers learn and improve from their past experiences or data without being explicitly programmed.

PAST EXPERIENCES----->PATTERN RECOGNITION----->PREDICTIONS

Applications of Machine Learning:-

Facebook NewsFeed
Facebook Photo Auto-tagging feature
Product recommendations by shopping portals
Automatic recommendation of movies to watch on various platforms like Netflix, Amazon Prime, Hotstar, etc.

Some of the Advanced Applications of Machine Learning:-

Identifying Frauds in Banking
Sentiment Analysis
Amazon Go
Chatbots
Self Driven Cars

Apart from Commercial Applications, Machine Learning has a tremendous influence on the way data-driven research is done today.

Finding distant Planets.
Discovering New Particles.
Analyzing DNA Sequences.
Personalized Cancer treatment, etc.

Why Machine Learning?

In the early days of intelligent applications used hand-coded rules of if and else decisions to process data or adjust to user input.

Think of a spam filter whose job is to move the appropriate incoming email messages to a spam folder. We could make a blacklist of words that would result in an email being marked as spam. This would be an example of using an expert-designed rule system to design an "intelligent" application.

How Machine Learning Works?

Machine Learning is the ability of machines to learn from past experiences to make predictions or recommendations.
Data is at the heart of Machine Learning.

In Tradition, We give Input to the Program and get Output as the result.

Input------>Program------>Output

But, In Machine Learning Model We give Data to the Machine Learning Model and get Intelligence(Algorithms, Rules) as a result.

Data------>ML Model------>Intelligence

Knowing Your Task & Knowing Your Data

Quite possibly the most important part of the Machine Learning process is understanding the data you are working with and how it relates to the task you want to solve. It won't be effective to randomly choose an algorithm and throw your data at it.

It is necessary to understand what is going on in your data set before you begin building a Model.

Note:-

Hey, I'm a beginner too. I'm sharing my journey. Hoping that someone might get benefit from this.
If you have any suggestions or questions for me ask me in the comment section.