Understanding the Machine Learning Algorithms Landscape…

Here is a list of all popular algorithms available for most data scientist. The real value is knowing when and where to applied them. I find that understanding the functionality of each category is more effective, then understand the difference among specific algorithm within each category. This approach speeds comprehension and recall (e.g. bigger blocks) when facing a real-life problem.

I will be working to build a playbook for when you would use each algorithm with the following considerations:

  • examples of applications in real-world
  • which contexts it performs well
  • if it can be used as a black box
  • ease of use and interpretation
  • how it handles missing data
  • enterprise version available or not
  • integration with existing analytics platforms or real-time systems
  • constraints on data (e.g. Naive Bayes performs poorly on correlated variables),
  • maintenance/scalability issues
  • distributed implementation needed
  • speed or computational complexity
  • can be easily blended with others

For more detail list refer here.

1. Regression Algorithms

  • Ordinary Least Squares Regression (OLSR)
  • Linear Regression
  • Logistic Regression
  • Stepwise Regression
  • Multivariate Adaptive Regression Splines (MARS)
  • Locally Estimated Scatterplot Smoothing (LOESS)

2. Instance-based Algorithms

  • k-Nearest Neighbour (kNN)
  • Learning Vector Quantization (LVQ)
  • Self-Organizing Map (SOM)
  • Locally Weighted Learning (LWL)

3. Regularization Algorithms

  • Ridge Regression
  • Least Absolute Shrinkage and Selection Operator (LASSO)
  • Elastic Net
  • Least-Angle Regression (LARS)

4. Decision Tree Algorithms

  • Classification and Regression Tree (CART)
  • Iterative Dichotomiser 3 (ID3)
  • C4.5 and C5.0 (different versions of a powerful approach)
  • Chi-squared Automatic Interaction Detection (CHAID)
  • Decision Stump
  • M5
  • Conditional Decision Trees

5. Bayesian Algorithms

  • Naive Bayes
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Averaged One-Dependence Estimators (AODE)
  • Bayesian Belief Network (BBN)
  • Bayesian Network (BN)

6. Clustering Algorithms

  • k-Means
  • k-Medians
  • Expectation Maximisation (EM)
  • Hierarchical Clustering

7. Association Rule Learning Algorithms

  • Apriori algorithm
  • Eclat algorithm

8. Artificial Neural Network Algorithms

  • Perceptron
  • Back-Propagation
  • Hopfield Network
  • Radial Basis Function Network (RBFN)

9. Deep Learning Algorithms

  • Deep Boltzmann Machine (DBM)
  • Deep Belief Networks (DBN)
  • Convolutional Neural Network (CNN)
  • Stacked Auto-Encoders

10. Dimensionality Reduction Algorithms

  • Principal Component Analysis (PCA)
  • Principal Component Regression (PCR)
  • Partial Least Squares Regression (PLSR)
  • Sammon Mapping
  • Multidimensional Scaling (MDS)
  • Projection Pursuit
  • Linear Discriminant Analysis (LDA)
  • Mixture Discriminant Analysis (MDA)
  • Quadratic Discriminant Analysis (QDA)
  • Flexible Discriminant Analysis (FDA)

11. Ensemble Algorithms

  • Boosting
  • Bootstrapped Aggregation (Bagging)
  • AdaBoost
  • Stacked Generalization (blending)
  • Gradient Boosting Machines (GBM)
  • Gradient Boosted Regression Trees (GBRT)
  • Random Forest

12. Other Algorithms

  • Computational intelligence (evolutionary algorithms, etc.)
  • Computer Vision (CV)
  • Natural Language Processing (NLP)
  • Recommender Systems
  • Reinforcement Learning
  • Graphical Models
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