AI and Machine Learning Algorithms

Contents

  1. Introduction to Artificial Intelligence and Machine Learning
    1. Understanding the History and Evolution of AI and ML
    2. Real-world Applications of AI and ML Algorithms
  2. Introduction to Machine Learning
  3. Foundations of Machine Learning :
    • Data, Datasets, and Data Preprocessing
    • Supervised, Unsupervised, and Reinforcement Learning
  4. Regression Algorithms
    1. Linear Regression
    2. Polynomial Regression
    3. Ridge Regression
    4. Lasso Regression
    5. Elastic Net Regression
    6. Support Vector Regression
  5. Classification Algorithms
    1. Logistic Regression
    2. Decision Trees
    3. Random Forests
    4. Support Vector Machines (SVMs)
    5. Naive Bayes
    6. K-Nearest Neighbors (KNN):
  6. Clustering Algorithms
    1. K-Means
    2. Hierarchical Clustering
    3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
    4. Gaussian Mixture Models (GMMs)
    5. Spectral Clustering:
    6. Fuzzy C-Means
  7. Association Rule Learning Algorithms
    1. Apriori
    2. FP-Growth
    3. ECLAT
    4. CARMA (Clustering and Association Rule Mining Algorithm):
    5. RuleFit
    6. Sequential Pattern Mining
  8. Dimensionality Reduction Algorithms
    1. Principal Component Analysis (PCA)
    2. Linear Discriminant Analysis (LDA)
    3. t-SNE (t-Distributed Stochastic Neighbor Embedding)
    4. Isomap (Isometric Mapping):
    5. Non-negative Matrix Factorization (NMF)
    6. Autoencoders
  9. Ensemble Learning Methods
    1. Bagging:
    2. Boosting:
    3. Random Forest
    4. AdaBoost:
    5. Gradient Boosting
    6. Stacking
  10. Deep Learning Algorithms
    1. Convolutional Neural Networks (CNNs)
    2. Recurrent Neural Networks (RNNs)
    3. Long Short-Term Memory (LSTM) Networks
    4. Generative Adversarial Networks (GANs):
    5. Deep Reinforcement Learning:
    6. Deep Belief Networks (DBNs)
    7. Transformer Networks
    8. Self-Organizing Maps (SOMs)
    9. Capsule Networks
    10. Radical Basis Function Networks (RBFNs)
  11. Reinforcement Learning Algorithms
    1. Q-Learning
    2. SARSA
    3. Deep Q-Networks (DQNs)
    4. Policy Gradient Methods
    5. Actor-Critic Methods:
    6. Monte Carlo Methods
  12. Natural Language Processing (NLP) Algorithms
    1. Tokenization and Text Preprocessing
    2. Named Entity Recognition (NER)
    3. Part-of-Speech Tagging (POS)
    4. Bag-of-Words and TF-IDF
    5. Word Embeddings (Word2Vec, GloVe)
    6. Sentiment Analysis
    7. Sequence-to-Sequence Models
    8. Recurrent Neural Networks (RNNs)
    9. Long Short-Term Memory (LSTM) Networks
    10. Transformer Models (e.g., BERT, GPT)
  13. Explainable AI and Interpretability
    1. Importance of Explainability in AI and ML
    2. Techniques for Model Interpretability
    3. LIME (Local Interpretable Model-agnostic Explanations)
    4. SHAP (SHapley Additive exPlanations)
  14. Hyperparameter Tuning and Model Evaluation
    1. Cross-Validation
    2. Grid Search and Random Search
    3. Model Performance Metrics
    4. Bias-Variance Tradeoff
  15. Anomaly Detection Algorithms:
    1. One-Class SVM
    2. Isolation Forest
    3. Local Outlier Factor (LOF)
  16. Hybrid Algorithms:
    1. Neuro-Fuzzy Systems
    2. Fuzzy Logic and Fuzzy Inference Systems
  17. Search and Optimization Algorithms:
    1. Hill Climbing
    2. Simulated Annealing
    3. Genetic Algorithms (GA)
    4. Ant Colony Optimization (ACO)
    5. Particle Swarm Optimization (PSO)
  18. Future Trends in AI and ML
    1. Reinforcement Learning Advancements
    2. Quantum Machine Learning
    3. AI in Healthcare
    4. AI in Autonomous Vehicles
    5. Ethical AI Governance
  19. Conclusion
    1. Recapitulation of Key Concepts
    2. Journey Towards Mastering AI and ML Algorithms

20. Bibliography

Information shared by : PALGUNI G T