https://github.com/eneskemalergin/machinelearning_beyond

Repository to store machine learning, artificial intelligence, and deep learning implementations with explanations
https://github.com/eneskemalergin/machinelearning_beyond

algorithms machine-learning

Last synced: about 1 year ago
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Repository to store machine learning, artificial intelligence, and deep learning implementations with explanations

• Host: GitHub
• URL: https://github.com/eneskemalergin/machinelearning_beyond
• Owner: eneskemalergin
• License: mit
• Created: 2015-10-12T14:45:30.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2018-04-17T09:19:13.000Z (about 8 years ago)
• Last Synced: 2025-05-07T09:15:42.799Z (about 1 year ago)
• Topics: algorithms, machine-learning
• Language: Jupyter Notebook
• Size: 8.37 MB
• Stars: 11
• Watchers: 0
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Machine Learning and Beyond

The Machine Learning algorithms are broken down in several categories. In the following mind map from [Data Science Central](http://www.datasciencecentral.com/profiles/blogs/a-tour-of-machine-learning-algorithms-1) we can see the summary:

![](http://api.ning.com/files/0qR8BrPZ-VZNxGOCc9HIXhtVdu4FIxXA3BKWwtrYkXQ0nvVO1yOJKU76E4LcCd3ln-bdReqYkhipURC00JjlWMTMOr340TB9/ML87i.png)

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