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# MACHINE LEARNING TYPES

There are many criteria where its type is classified, here I’m going to simply speak only on Human Supervision only

- Supervised learning
- Unsupervised learning
- Semisupervised learning
- Reinforcement Learning

Another type is whether it can learn on-fly or not

- online versus batch learning

Another type is whether it can find patterns or not

- instance-based versus model-based learning

**SUPERVISED LEARNING**

In *supervised learning*, the training set you feed to the algorithm includes the desired solutions, called Labels.

Supervised learning is simply learning where it is told to identify the objects, that is dissecting supervised learning, consider yourself inside a classroom where your teacher(Experienced person)will teach you about everything that you are learning, or as a small child, your parent(Experienced person) will teach or help you to identify dogs vs cat as they provide a detailed difference between it (Labelling).

To be precise, similarly here we teach the machine with a labeled dataset.

Supervised learning is classified as REGRESSION and CLASSIFICATION based on their Data to be predicted

Regression — where it predicts the continuous data(Present in all timesteps)

Classification — where it predicts the discrete data(Present in particular timesteps only)

ALGORITHMS COMES UNDER SUPERVISED LEARNING :

Algorithms are simply the Teacher

REGRESSION :

- Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Decision Tree Regression
- Random Forest Regression
- Support Vector Regression
- KNN Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
- Artificial Neural Networks(Deep learning)

CLASSIFICATION :

- Logistic regression
- Decision Tree classifier
- Random Forest Classifier
- Support Vector Classifier
- KNN Classifier
- Artificial Neural Network(Deep Learning)

**UNSUPERVISED LEARNING**

In *unsupervised learning*, as you might guess, the training data is unlabeled. Same as a Situation where You will be learning things from your own experience without a teacher teaching you(Unlabeled), that is you will be finding the nuke and corner of your own that’s how unsupervised learning works.

Here are some of the most important unsupervised learning algorithms

@ Clustering

1.K-Means

2.DBSCAN

3.Hierarchical Cluster Analysis (HCA)

@ Anomaly detection and novelty detection

1.One-class SVM

2.Isolation Forest

@ Visualization and dimensionality reduction

1.Principal Component Analysis (PCA)

2.Kernel PCA

3.Locally Linear Embedding (LLE)

4.t-Distributed Stochastic Neighbor Embedding (t-SNE)

@ Association rule learning

1.Apriori

2.Eclat

**SEMI-SUPERVISED LEARNING**

Since labeling data is usually time-consuming and costly, you will often have plenty of unlabeled instances and few labeled instances. Some algorithms can deal with data that’s partially labeled. This is called *semisupervised learning.*

eg. Google Photos is the best example where based on the one photo you say it as yours(Supervised)and the model automatically finds all the other photos of yours(face)and will make a new album of it(Unsupervised).

**REINFORCEMENT LEARNING**

*Reinforcement Learning* is a very different beast. The learning system, called an *agent* in this context, can observe the environment, select and perform actions, and get *rewards* in return (or *penalties* in the form of negative rewards). It must then learn by itself what is the best strategy, called a *policy*, to get the most reward over time. A policy defines what action the agent should choose when it is in a given situation. This is a very good example of how typically a human learns.

AI/ML

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