42 Most commonly used Machine Learning Algorithms

42 Most commonly used Machine Learning Algorithms — Murat Durmus

The following briefly overviews the 42 most commonly used machine learning algorithms. A more detailed description can be found in the book A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples).

Supervised Learning

• Decision Tree: A tree-like model of decisions and their possible consequences.
• Linear Regression: Predicts a dependent variable value based on independent variables.
• Logistic Regression: Estimates the probability of a binary outcome based on one or more independent variables.
• Naive Bayes Classifiers: Applies Bayes’ theorem with strong independence assumptions between features.
• K-Nearest Neighbour: Classifies data points based on the points that are most similar to it.
• Support Vector Machine: Finds the hyperplane that best divides a dataset into classes.
• Random Forests: An ensemble of decision trees, typically used for classification and regression.
• Gradient Boosting Machine: Builds an additive model in a forward stage-wise fashion.
• XGBOOST: An optimized distributed gradient boosting library.
• ADABOOST: An ensemble method that combines multiple weak classifiers to create a strong classifier.

Unsupervised Learning

• K-Means: A method of vector quantization for cluster analysis.
• Agglomerative Clustering: A hierarchical clustering method using a bottom-up approach.
• DBSCAN: Density-Based Spatial Clustering of Applications with Noise.
• Hierarchical Clustering: Builds a hierarchy of clusters by merging or splitting them successively.
• GMM (Gaussian Mixture Model): A probabilistic model for representing normally distributed subpopulations.
• Factor Analysis of Correspondences: Used in data analysis for categorical data.
• Independent Component Analysis: A computational method for separating a multivariate signal into additive subcomponents.
• Mean Shift: A non-parametric feature-space analysis technique for locating the maxima of a density function.
• Principal Component Analysis: A technique for reducing the dimensionality of datasets.

Deep Learning

• Convolutional Neural Network: Mainly used for image recognition and processing.
• Deep Q-Learning: A reinforcement learning algorithm that combines Q-Learning with deep neural networks.
• EfficientNet: A scaling method for convolutional neural networks.
• GAN (Generative Adversarial Network): A class of machine learning frameworks designed by opposing networks.
• GPT-3: An autoregressive language model that uses deep learning to produce human-like text.
• LSTM (Long Short-Term Memory): A type of recurrent neural network capable of learning order dependence in sequence prediction problems.
• Recurrent Neural Network: A class of neural networks where connections between nodes form a directed graph along a temporal sequence.
• ResNet: A deep convolutional network for image recognition.
• Wavenet: A deep neural network for generating raw audio waveforms.
• Graph Neural Networks: Neural networks that capture the dependence of graphs via message passing between the nodes of graphs.
• Spatial Temporal Graph Convolutional Networks: For dynamic graph-structured data.
• MobileNet: Efficient convolutional neural networks for mobile vision applications.
• BERT (Bidirectional Encoder Representations from Transformers): Designed to understand the context of a word in search queries.

Optimization Algorithms

• Gradient Descent: An iterative optimization algorithm for finding the minimum of a function.
• Stochastic Gradient Descent: A stochastic approximation of the gradient descent optimization.
• ADAM Optimization: An algorithm for first-order gradient-based optimization of stochastic objective functions.

Others

• ARMA/ARIMA Model: Used for forecasting time series data.
• Hidden Markov Model (HMM): A statistical Markov model in which the system being modeled is assumed to be a Markov process with unobservable states.
• Isolation Forest: An algorithm for anomaly detection.
• Multimodal Parallel Network: Used for processing and integrating information from multiple different data sources or modalities.

Murat

A more detailed description can be found in the book A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples).

A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples). (Arificial…