BrewedCoffee / ml-testing

Algorithms to Implement:

Regerssion

• Linear Regression

Classification

• Logistic Regression
• SVM
• Naive Bayes
• kNN
• Random Forest
• Learning Vector Quantization
• Self-Organizing Map
• Locally Weighted Learning

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

Artificial Neural Network Algorithms

• Perceptron
• Multilayer Perceptrons (MLP)
• Back-Propagation
• Stochastic Gradient Descent
• Hopfield Network
• Radial Basis Function Network (RBFN)

Deep Learning Algorithms

• Convolutional Neural - Network (CNN)
• Recurrent Neural Networks - (RNNs)
• Long Short-Term Memory - Networks (LSTMs)
• Stacked Auto-Encoders
• Deep Boltzmann Machine (DBM)
• Deep Belief Networks (DBN)

Unsupervised

• K-Means

Regularization Algorithms

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

Analytical Tools

• Dimensionality Reduction Algorithms
• Gradient Boosting algorithms

Data

• Custom Datasets
• Iris Dataset

References

• Algorithm List
• Step-By-Step

Potential takeaways:

• Accuracy
• Speed
• Which algorithms are better (and worse) for what
• Personal preferences