Machine Learning Algorithms And Important Topics

Updated: Aug 4, 2020



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

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 (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 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 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 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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