Machine learning is a rapidly evolving field with numerous algorithms designed to tackle various data science challenges. This article provides an overview of 101 machine learning algorithms, categorized by their primary functions.

## Classification Algorithms

Classification algorithms predict outcome classes for given datasets. Here are some key examples:

- Logistic Regression: A statistical method for predicting binary outcomes.
- Naive Bayes: A probabilistic classifier based on Bayes’ theorem.
- Support Vector Machines (SVM): Algorithms that create a hyperplane to separate classes.
- K-Nearest Neighbors (KNN): Classifies based on the majority class of nearest neighbors.
- Decision Trees: Tree-like models of decisions and their possible consequences.

## Regression Algorithms

Regression algorithms examine relationships between variables. Some popular regression algorithms include:

- Linear Regression: Models linear relationships between variables.
- Polynomial Regression: Fits a nonlinear relationship to data.
- Ridge Regression: Linear regression with L2 regularization.
- Lasso Regression: Linear regression with L1 regularization.
- Elastic Net: Combines L1 and L2 regularization.

## Neural Networks

Neural networks are artificial models inspired by the human brain. Some common types include:

- Perceptron: The simplest form of neural network.
- Multilayer Perceptron (MLP): A feedforward network with multiple layers.
- Convolutional Neural Networks (CNN): Specialized for processing grid-like data.
- Recurrent Neural Networks (RNN): Process sequential data with loops.
- Long Short-Term Memory (LSTM): A type of RNN that can learn long-term dependencies.

## Anomaly Detection

Anomaly detection algorithms find rare occurrences or suspicious events in data:

- Isolation Forest: Isolates anomalies in the feature space.
- One-Class SVM: Learns a decision boundary to classify new data as similar or different.
- Local Outlier Factor (LOF): Measures local deviation of density of a given sample.

## Dimensionality Reduction

These algorithms reduce the number of random variables in a dataset:

- Principal Component Analysis (PCA): Reduces dimensions by finding orthogonal linear combinations.
- t-SNE: Visualizes high-dimensional data in 2D or 3D space.
- Linear Discriminant Analysis (LDA): Finds a linear combination of features to separate classes.

## Ensemble Methods

Ensemble methods combine multiple algorithms to improve overall performance:

- Random Forest: Combines multiple decision trees.
- Gradient Boosting: Builds models sequentially to correct errors.
- AdaBoost: Adjusts weights of instances to focus on hard-to-classify examples.

## Clustering Algorithms

Clustering assigns labels to unlabeled data based on patterns:

- K-Means: Partitions data into K clusters based on centroids.
- DBSCAN: Density-based clustering for discovering clusters of arbitrary shape.
- Hierarchical Clustering: Creates a tree of clusters.

## Association Rule Learning

These algorithms uncover associations between items:

- Apriori Algorithm: Finds frequent itemsets in a database.
- FP-Growth Algorithm: An improved method for mining frequent patterns.

## Regularization Techniques

Regularization prevents overfitting:

- L1 Regularization (Lasso): Adds absolute value of magnitude of coefficients as penalty term.
- L2 Regularization (Ridge): Adds squared magnitude of coefficients as penalty term.
- Elastic Net: Combines L1 and L2 regularization.

This comprehensive list of 101 machine learning algorithms covers a wide range of techniques used in data science. For more detailed information on each algorithm and when to use them, refer to the cheat sheets provided by Scikit-Learn.

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101 Machine Learning Algorithms: A Comprehensive Guide