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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