AN OVERVIEW ON MACHINE LEARNING ALGORITHMS

If you are learning machine learning for getting a high profile data science job then you can’t miss out learning these best machine learning algorithms.

Here, we will go through classification and regression algorithms. While there are many more algorithms that are present in the arsenal of machine learning, our focus will be on the most popular machine learning algorithms.

These ML algorithms are quite essential for developing predictive modeling and for carrying out classification and prediction. These ML algorithms are the most useful for carrying out prediction and classification in both supervised as well as unsupervised scenarios.

REGRESSION:

Before knowing what is linear regression, let us get ourselves accustomed to regression. Regression is a method of modelling a target value based on independent predictors. This method is mostly used for forecasting and finding out cause and effect relationship between variables. Regression techniques mostly differ based on the number of independent variables and the type of relationship between the independent and dependent variables.

EXAMPLE

 Price of the home changes based on factors like location, size , economic crisis or pandemic situations.

 

TYPES OF REGRESSION

The following are types of regression.

1. Simple Linear Regression

2. Polynomial Regression

3. Support Vector Regression

4. Decision Tree Regression

5. Random Forest Regression

This is how the graph after performing Regression Algorithm looks like 

CLASSIFICATION:

Classification is the process of recognizing, understanding, and grouping ideas and objects into preset categories or “sub-populations.” Using pre-categorized training datasets, machine learning programs use a variety of algorithms to classify future datasets into categories.

In short, classification is a form of “pattern recognition,” with classification algorithms applied to the training data to find the same pattern (similar words or sentiments, number sequences, etc.) in future sets of data.

Using classification algorithms, which we’ll go into more detail about below, text analysis software can perform things like sentiment analysis to categorize unstructured text by polarity of opinion (positive, negative, neutral, and beyond). 

EXAMPLE

• Analysis of the customer data to predict whether he will buy computer accessories (Target class: Yes or No)

• Classifying fruits from features like color, taste, size, weight (Target classes: Apple, Orange, Cherry, Banana)

• Gender classification from hair length (Target classes: Male or Female)

TYPES OF CLASSIFICATION ALGORITHMS

1. Logistic Regression

2. Naïve Bayes

3. Stochastic Gradient Descent

4. K-Nearest Neighbours

5. Decision Tree

6. Random Forest

7. Support Vector Machine

This is how the graph after performing Classification Algorithm looks like 

Next chapter let's discuss in depth about each of these algorithms. Happy learning!!