As per increasing demands by different organization and software industries, here we provide only simple ways to learn various machine learning algorithms and some useful topics which are used now a day most of the areas, which is given below:

**Supervised Learning****Unsupervised Learning****Ensemble Learning****Reinforcement Learning****Predictive****modeling****Regression analysis****Classification****Perceptrons****TimeSeries data analysis**

** Supervised Learning: **This algorithm consists of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables)

It divided into two categories:

Classification

Regression

__Example:__

Linear Regression, Decision Tree, Random Forest, KNN, Logistic Regression, etc.

** Unsupervised Learning: **In this algorithms we do not have any target or outcome variable to predict / estimate. It is used for the clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention or researches.

It divided into below categories:

Deep Learning

Other

**Deep Learning:**

Representation Learning

1. Mutual Information

2. Disentanglement

3. Information bottleneck

Generative Models

1. GANs

2. VAE

**Other:**

Dimension Reduction

1. PCA

2. t-SNE

Clustering

1. K-mean

2. GMMs

3. HMMs

__Ensemble Learning__

__Ensemble Learning__

This algorithm used to improve the result by combining more than one algorithms or method.

This approach provides a better result than the single machine Learning algorithm, so most of the cases it takes the place first in machine learning challenges.

It is used to decrease variance(bagging), bias(boosting), or improve predictions.

It divided into two categories:

*sequential ensemble methods**parallel ensemble methods*

__Sequential____ensemble methods__

**Example:**

Adaboost

__Parallel____ensemble methods__

**Example:**

Random Forest

__Reinforcement Learning__

__Reinforcement Learning__

Reinforcement Learning (RL) is a branch of machine learning concerned with actors, or **agents**, taking actions is some kind of **environment** in order to maximize some type of **reward** that they collect along the way.

Some important features of Reinforcement Learning:

Two types of reinforcement learning are 1)

**Positive**2)**Negative**Two widely used learning model are 1)

**Markov Decision Process**2)**Q learning**Reinforcement Learning method works on interacting with the environment, whereas the supervised learning method works on given sample data or example.

** **__Predictive modeling __

__Predictive modeling__

Predictive modeling is an important feature of the machine learning task, it involves some basic steps:

Descriptive analysis on the Data

Data treatment (Missing value and outlier fixing)

Data Modelling

Estimation of performance

__Regression analysis__

__Regression analysis__

Regression analysis is a form of predictive modelling technique that investigates the relationship between a **dependent **(target) and the **independent variable (s)** (predictor).

This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables.

**Types:**

Linear Regression

Logistic Regression

Polynomial Regression

Stepwise Regression

Ridge Regression

Lasso Regression

ElasticNet Regression

__Classification analysis__

__Classification analysis__

Classification is a process of categorizing a given set of data into classes, It can be performed on both structured or unstructured data.

Classification Algorithms

Logistic Regression

Naive Bayes

Stochastic Gradient Descent

K-Nearest Neighbors

Decision Tree

Random Forest

Artificial Neural Network

Support Vector Machine

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