ALL MACHINE LEARNING ALGORITHMS – COMPLETE LIST

1️⃣ Supervised Learning Algorithms

🔹 Regression Algorithms

Linear Regression
Multiple Linear Regression
Polynomial Regression
Ridge Regression (L2)
Lasso Regression (L1)
Elastic Net Regression
Bayesian Linear Regression
Quantile Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Gradient Boosting Regression
XGBoost Regression
LightGBM Regression
CatBoost Regression
AdaBoost Regression
Extra Trees Regression

🔹 Classification Algorithms

Logistic Regression
K-Nearest Neighbors (KNN)
Naive Bayes

Gaussian NB
Multinomial NB
Bernoulli NB

Decision Tree Classifier
Random Forest Classifier
Support Vector Machine (SVM)
Gradient Boosting Classifier
AdaBoost Classifier
XGBoost Classifier
LightGBM Classifier
CatBoost Classifier
Extra Trees Classifier
Linear Discriminant Analysis (LDA)
Quadratic Discriminant Analysis (QDA)
Perceptron
Passive Aggressive Classifier

2️⃣ Unsupervised Learning Algorithms

🔹 Clustering

K-Means
K-Medoids
Hierarchical Clustering

Agglomerative
Divisive

DBSCAN
HDBSCAN
Mean Shift
OPTICS
BIRCH
Spectral Clustering
Affinity Propagation
Fuzzy C-Means

🔹 Dimensionality Reduction

Principal Component Analysis (PCA)
Kernel PCA
Linear Discriminant Analysis (LDA)*
Independent Component Analysis (ICA)
t-SNE
UMAP
Autoencoders
Truncated SVD
Factor Analysis
Isomap
Locally Linear Embedding (LLE)

*LDA can be supervised or unsupervised depending on use.

🔹 Association Rule Learning

Apriori
Eclat
FP-Growth

🔹 Anomaly / Outlier Detection

Isolation Forest
One-Class SVM
Local Outlier Factor (LOF)
Elliptic Envelope
HBOS
Autoencoder-based Anomaly Detection

3️⃣ Semi-Supervised Learning Algorithms

Self-Training
Co-Training
Label Propagation
Label Spreading
Pseudo-Labeling
Graph-Based Semi-Supervised Learning

4️⃣ Reinforcement Learning Algorithms

🔹 Value-Based

Q-Learning
SARSA
Deep Q Network (DQN)
Double DQN
Dueling DQN

🔹 Policy-Based

REINFORCE
Policy Gradient
Proximal Policy Optimization (PPO)
Trust Region Policy Optimization (TRPO)

🔹 Actor–Critic

A2C
A3C
DDPG
TD3
SAC

5️⃣ Deep Learning Algorithms

🔹 Neural Networks

Artificial Neural Network (ANN)
Deep Neural Network (DNN)
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
LSTM
GRU

🔹 Advanced Deep Learning

Autoencoders
Variational Autoencoders (VAE)
Generative Adversarial Networks (GAN)
Deep Belief Networks (DBN)
Transformers
Vision Transformers (ViT)
Graph Neural Networks (GNN)

6️⃣ Time Series Algorithms

ARIMA
SARIMA
SARIMAX
Holt-Winters
Exponential Smoothing
Prophet
LSTM for Time Series
Temporal Convolutional Networks (TCN)

7️⃣ Probabilistic & Bayesian Models

Hidden Markov Models (HMM)
Bayesian Networks
Gaussian Processes
Markov Chain Monte Carlo (MCMC)
Conditional Random Fields (CRF)

8️⃣ Evolutionary & Optimization Algorithms

Genetic Algorithm
Genetic Programming
Differential Evolution
Particle Swarm Optimization
Ant Colony Optimization
Simulated Annealing

9️⃣ Online / Incremental Learning

Stochastic Gradient Descent (SGD)
Hoeffding Tree
Online Naive Bayes
Passive-Aggressive Algorithms

🔟 AutoML & Meta-Learning

AutoML
Neural Architecture Search (NAS)
Meta-Learning
Few-Shot Learning
Transfer Learning

🎯 Interview Tip (Very Important)

You are NOT expected to know internals of all algorithms.

Interviewers usually test:

Linear / Logistic Regression
Decision Trees
Random Forest
XGBoost
SVM
K-Means
PCA
Neural Networks basics