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General Purpose Machine Learning Algorithms - A Summary (#machinelearning)(#MLAlgorithms)(#ipumusings)

General Purpose Machine Learning Algorithms - A Summary

General Purpose Machine Learning Algorithms - A Summary (#machinelearning)(#MLAlgorithms)(#ipumusings)

In layman terms, machine learning is a process that teaches a machine to think like a human being and perform a set of operations. Like continuous learning by our brains improves our intelligence and decision making. Similarly, machines work in a similar fashion.

Here is a summary of the most commonly used Machine Learning (ML) algorithms that might help you pick up the right one as per your requirements. Most of the ML algorithms in the list are multi-purpose. Please note the list is not inclusive.


1. Ordinary Least Squares Regression (OLSR)

2. Linear Regression

3. Logistic Regression

4. Stepwise Regression

5. Multivariate Adaptive Regression Splines (MARS)

6. Locally Estimated Scatterplot Smoothing (LOESS)

7. Jackknife Regression


1. Ridge Regression

2. Least Absolute Shrinkage and Selection Operator (LASSO)

3. Elastic Net

4. Least-Angle Regression (LARS))

Bayesian Algorithms

1. Naive Bayes

2. Gaussian Naive Bayes

3. Multinomial Naive Bayes

4. Bayesian Network (BN)

5. Hidden Markov Models

6. Averaged One-Dependence Estimators (AODE)

7. Bayesian Belief Network (BBN)

8. Conditional random fields (CRFs)

Decision Tree

1. Classification and Regression Tree (CART)

2. Iterative Dichotomiser 3 (ID3)

3. Chi-squared Automatic Interaction Detection (CHAID)

4. Decision Stump

5. Grading Boosting Machines

6. Random Forests

7. Conditional Decision Trees

Instance-based ML Algorithms

(Note: Instance-based algorithms are also called cake-based or memory-based)

1. k-Nearest Neighbour (kNN)

2. Learning Vector Quantization (LVQ)

3. Self-Organizing Map (SOM)

4. Locally Weighted Learning (LWL)


1. Single-linkage clustering

2. k-Means

3. k-Medians

4. Expectation Maximisation (EM)

5. Hierarchical Clustering

6. Fuzzy clustering

7. Non Negative Matrix Factorization

8. latent Dirichlet allocation (LDA)

Dimensionality reduction base ML Algorithms

1.Principal Component Analysis (PCA)

2. Principal Component Regression (PCR)

3. Partial Least Squares Regression (PLSR)

4. Sammon Mapping

5. Multidimensional Scaling (MDS)

6. Projection Pursuit

7. Discriminant Analysis (LDA, MDA, QDA, FDA)

Associated rule

1. Apriori

2. FP-Growth

Deep learning

1. Deep Boltzmann Machine (DBM)

2. Deep Belief Networks (DBN)

3. Convolutional Neural Network (CNN)

4. Stacked Auto-Encoders


1. Logit Boost (Boosting)

2. Bootstrapped Aggregation (Bagging)

3. AdaBoost

4. Stacked Generalization (blending)

5. Gradient Boosting Machines (GBM)

6. Gradient Boosted Regression Trees (GBRT)

7. Random Forest

Neural networks

1. Autoencoders

2. Perceptron

3. Back-Propagation

4. Hopfield Network

5. Radial Basis Function Network (RBFN)

6. Backpropagation

7. Self Organizing Map

8. Hopfield networks

9. Spiking Neural Networks

10. Boltzmann machines

11. Restricted Boltzmann Machines

12. Learning Vector quantization (LVQ)


1. Support Vector Machines (SVM)

2. Inductive Logic Programming (ILP)

3. Evolutionary Algorithms

4. Reinforcement Learning 


    - Temporal Difference,

    - State-Action-Reward-State-Action (SARSA))

5. Page Rank

6. Information Fuzzy Network (IFN)

7. Conditional Random Fields (CRF)

8. Hidden Markov models

9. Poisson regression.

👉See Also:

1. Automation - Need Of An Hour - Post Covid19 Pandemic

2.  Rise of Machines - New Cloud Trends Setting The Future

About the Author

Divij Handa, studying at University School of Information & Communication Technology, GGS Indraprastha University, Delhi, India. He wants to pursue his career in machine learning, artificial intelligence and cloud computing.