ML #2 | Major Machine Learning Techniques

Major Machine Learning Techniques

Image by mohamed Hassan from Pixabay

This is my second blog of the Machine Learning series. If you're new to ML then I'll recommend you to start with the first one ;)

So, let's come to the topic, Machine Learning is implemented using many pre build techniques and algorithms, here we'll be having a brief surface view of them and then in my upcomming blogs I'll cover them all in depth.

Linear Regression

1. Regression / Estimation

The Regression / Estimation technique is used for predicting continuous values.

Eg:- 

• Predicting things like the price of house based on its characteristics.
• Estimate the Co2 emission from a car's engine based on the characteristics of car.

Classification

2. Classification

A classification technique is used for predicting the class or category of a case.

Eg:-

• Identifying the type of cancer in a cell
• Detecting whether an e-mail is spam or not

Clustering

3. Clustering

Clustering is a technique of grouping of similar cases.

Eg:-

• Customer Segmentation in banking field
• Customer segmentation in E-commerce company like segregating customers that just scroll the website, or the ones that buy more of expensive things etc.

Association

4. Association

Association technique is used for finding items or events that often co-occur.

Eg:-

• Grocery items that are usually bought together by a particular customer
• Anomaly detection like credit card fraud detection

Sequence Mining

5. Sequence Mining

Sequence mining is used for predicting the next event, for example the click strams in a website.

Eg:-

• Customer buying sequences like first buy computer, then pen drive, then webcam etc.

Dimension Reduction

6. Dimension Reduction

There are many diverse examples of high dimensional datasets that are diffivult to process at once: like videos, e-mails, user logs, satellite observations etc.

For such data, we need to throw away the unnecessary and noisy dimensions and keep the most informative ones.

That's why we use dimension reduction techniques.

Eg:-

• E-mail Classification

Recommendation System

7. Recommendation System

This associates people's preferences with others who have similar tastes, and recommends new items to them, such as books or movies.

        

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