## Association Mining for Machine Learning

Association Mining

Naive Algorithm

Apriori Algoorithm

• No

## Description

This course covers the working Principle of Association Mining and its various concepts like Support, Confidence, and Life in a very simplified manner. This course discusses about Naive Algorithm and Apriori Algorithm for finding Association Mining rules by taking lot of examples. All of these algorithms has been explained by taking working examples.

## Who this course is for:

• Students taking Machine Learning or Data Mining Course
• Machine Learning Enthusiast
• Students preparing for placement tests and interviews

## Machine Learning Cheat Sheet

When working with machine learning, it’s easy to try them all out without understanding what each model does, and when to use them. In this cheat sheet, you’ll find a handy guide describing the most widely used machine learning models, their advantages, disadvantages, and some key use-cases.

## Supervised Learning

Supervised learning models are models that map inputs to outputs, and attempt to extrapolate patterns learned in past data on unseen data. Supervised learning models can be either regression models, where we try to predict a continuous variable, like stock prices—or classification models, where we try to predict a binary or multi-class variable, like whether a customer will churn or not. In the section below, we’ll explain two popular types of supervised learning models: linear models, and tree-based models.

### Linear Models

In a nutshell, linear models create a best-fit line to predict unseen data. Linear models imply that outputs are a linear combination of features. In this section, we’ll specify commonly used linear models in machine learning, their advantages, and disadvantages.

### Tree-based models

In a nutshell, tree-based models use a series of “if-then” rules to predict from decision trees. In this section, we’ll specify commonly used linear models in machine learning, their advantages, and disadvantages.

## Unsupervised Learning

Unsupervised learning is about discovering general patterns in data. The most popular example is clustering or segmenting customers and users. This type of segmentation is generalizable and can be applied broadly, such as to documents, companies, and genes. Unsupervised learning consists of clustering models, that learn how to group similar data points together, or association algorithms, that group different data points based on pre-defined rules.