Machine Learning Algorithms
Overview of Popular ML Algorithms
Categories of ML Algorithms
• 1. Supervised Learning
• 2. Unsupervised Learning
• 3. Semi-Supervised Learning
• 4. Reinforcement Learning
Supervised Learning Algorithms
• - Linear Regression
• - Logistic Regression
• - Decision Trees
• - Random Forest
• - Support Vector Machines (SVM)
• - K-Nearest Neighbors (KNN)
• - Naïve Bayes
Unsupervised Learning Algorithms
• - K-Means Clustering
• - Hierarchical Clustering
• - DBSCAN (Density-Based Spatial Clustering)
• - PCA (Principal Component Analysis)
• - Apriori Algorithm (Association Rules)
Reinforcement Learning Algorithms
• - Q-Learning
• - Deep Q-Networks (DQN)
• - SARSA (State-Action-Reward-State-Action)
• - Policy Gradient Methods
Ensemble Methods
• - Bagging (e.g., Random Forest)
• - Boosting (e.g., AdaBoost, Gradient Boosting)
• - Stacking (e.g., using multiple models)

Machine_Learning_Algorithms_Presentation.pptx

  • 1.
    Machine Learning Algorithms Overviewof Popular ML Algorithms
  • 2.
    Categories of MLAlgorithms • 1. Supervised Learning • 2. Unsupervised Learning • 3. Semi-Supervised Learning • 4. Reinforcement Learning
  • 3.
    Supervised Learning Algorithms •- Linear Regression • - Logistic Regression • - Decision Trees • - Random Forest • - Support Vector Machines (SVM) • - K-Nearest Neighbors (KNN) • - Naïve Bayes
  • 4.
    Unsupervised Learning Algorithms •- K-Means Clustering • - Hierarchical Clustering • - DBSCAN (Density-Based Spatial Clustering) • - PCA (Principal Component Analysis) • - Apriori Algorithm (Association Rules)
  • 5.
    Reinforcement Learning Algorithms •- Q-Learning • - Deep Q-Networks (DQN) • - SARSA (State-Action-Reward-State-Action) • - Policy Gradient Methods
  • 6.
    Ensemble Methods • -Bagging (e.g., Random Forest) • - Boosting (e.g., AdaBoost, Gradient Boosting) • - Stacking (e.g., using multiple models)