# Machine learning (abstract)

what is machine learning and its type all concept of machine learning

**Machine learning :-**

Machine Learning systems can be classified according to the amount and type of supervision they get during training.

in machine learning there are various method and each method specify with specific problem for example if we are solving SVM problem than we cannot use linear regression because linear regression is different concept and SVM is different concept.

**Type of Machine learning:-**

**Supervised Machine learning**

**Unsupervised Learning**

**semi supervised learning learning**

**Reinforcement Learning**

** Supervised learning**:-

In supervise learning you train model for predicting output you have data and that data set you clean and train ,test after that model will be create

for example to check mail spam or not you will be use classification algorithm that is logistic regression in logistic regression only true or false output .in logistics regression we use sigmoid function ,sigmoid function convert value into 0 or 1 .

or predicting value we use Linear regression in that depend independent i will explain in detail.

in supervised learning we use model that helps to desired output . algorithm in supervised learning

typical example of supervised learning is that to check mail spam or not or to predict price of home using linear regression that is depend and independent variable

Linear Regression ,logistic Regression support Vector machine k-Nearest Neighbors

*Support Vector Machine :*

A Support Vector Machine (SVM) is a very powerful and versatile Machine Learning model, capable of performing linear or nonlinear classification, regression, and even outlier detection. It is one of the most popular models in Machine Learning, and any‐ one interested in Machine Learning should have it in their toolbox. SVMs are partic‐ ularly well suited for classification of complex but small- or medium-sized datasets

**K-Nearest Neighbors:**

it is also called as lazy learning because in that already k value defined and when new data enter it find out which K value nearest using distance formula and after that these data will under the any K value

**Unsupervised Learning:**

in that unsupervised learning data will group with un label data in short grouping of the data for example youtube algorithm which video you watched after that youtube algorithm automatically grouped based on what you watch if you watch video song and any news ,sport than youtube algorithm group this type algorithm called unsupervised learning.

Here are some of the most important unsupervised learning algorithms

Clustering

Visualization and dimensionality reduction

Association rule learning

**semi supervised learning learning**

semi supervised learning is combination of supervised and unsupervised learning we know that in supervise learning predicting data linearly and in un supervise learning we classifying data that is grouping data with labeling .

other type i will share next blog