Machine Learning and Beyond

The Machine Learning algorithms are broken down in several categories. In the following mind map from Data Science Central we can see the summary:

In this repository, I am going to follow the mind map and store the algorithms, class notes, real life applications, snippets/scripts, and more.

0. Others

• Stanford Machine Learning Class

1. Regression Algorithms

• 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