ML Algorithms:

Here's a comprehensive list of common machine learning algorithms, organized by category:

Supervised Learning:

Regression Algorithms:

• Linear Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
• Ridge Regression
• Lasso Regression
• Elastic Net
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression
• Gradient Boosting Regression (e.g., XGBoost, LightGBM)
• K-Nearest Neighbors (KNN) Regression
• Bayesian Regression

Classification Algorithms:

• Logistic Regression
• Naive Bayes
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Bernoulli Naive Bayes
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Decision Tree
• Random Forest
• Gradient Boosting Classifiers (e.g., AdaBoost, XGBoost, LightGBM)
• Neural Networks
  • Perceptron
  • Multilayer Perceptron (MLP)
• Linear Discriminant Analysis (LDA)
• Quadratic Discriminant Analysis (QDA)
• Ensemble Methods
  • Bagging
  • Boosting
  • Stacking

Unsupervised Learning:

Clustering Algorithms:

• K-Means
• Hierarchical Clustering
  • Agglomerative
  • Divisive
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• Gaussian Mixture Models (GMM)
• Mean Shift
• OPTICS (Ordering Points To Identify the Clustering Structure)

Dimensionality Reduction:

• Principal Component Analysis (PCA)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• Linear Discriminant Analysis (LDA)
• Independent Component Analysis (ICA)
• Autoencoders

Association Rule Learning:

• Apriori
• Eclat

Semi-Supervised Learning:

• Self-Training
• Co-Training
• Transductive SVM

Reinforcement Learning:

• Q-Learning
• SARSA
• Deep Q-Network (DQN)
• Policy Gradient Methods
  • REINFORCE
  • Actor-Critic
• Monte Carlo Tree Search

Deep Learning Algorithms:

• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
  • Long Short-Term Memory (LSTM)
  • Gated Recurrent Unit (GRU)
• Autoencoders
• Generative Adversarial Networks (GANs)
• Transformers

Other Techniques:

• Ensemble Methods
  • Voting Classifiers
  • Stacking
• Feature Selection Algorithms
• Anomaly Detection
  • Isolation Forest
  • One-class SVM

Resources to learn Machine Learning

1. https://www.kaggle.com/
2. https://resources.nvidia.com/en-us-event-slides/free-courses
3. https://www.geeksforgeeks.org/machine-learning/

ML Projects for Beginners

1. https://www.geeksforgeeks.org/machine-learning-projects/
2. https://data-flair.training/blogs/machine-learning-project-ideas/