AI & ML Education

AI & ML Topics

Introduction to AI and ML

  • Definition of Artificial Intelligence (AI) and Machine Learning (ML)
  • Applications of AI and ML in various domains
  • Difference between AI, ML, and Deep Learning

Linear Regression

  • Simple linear regression
  • Multiple linear regression
  • Gradient descent algorithm

Classification

  • Logistic Regression
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)

    • Clustering

    • K-means clustering
    • Hierarchical clustering
    • Density-based clustering (DBSCAN)
    • Evaluation metrics for clustering

      • Feature Engineering

      • Handling missing data
      • Feature scaling
      • One-hot encoding

      Evaluation Metrics

      • Accuracy
      • Precision, Recall, F1-Score
      • Confusion Matrix

      Overfitting and Underfitting

      • Bias-variance tradeoff
      • Regularization techniques (L1, L2 regularization)

      Cross-Validation

      • k-fold cross-validation
      • Leave-One-Out cross-validation

      Bias-Variance Tradeoff

      • Understanding bias and variance

      Introduction to Neural Networks

      • Perceptrons
      • Activation functions (sigmoid, tanh, ReLU)

      Intermediate Topics

      Deep Learning

      • Backpropagation algorithm
      • Optimization algorithms (SGD, Adam, RMSprop)

      Convolutional Neural Networks (CNNs)

      • Convolutional layers
      • Pooling layers

      Recurrent Neural Networks (RNNs)

      • Long Short-Term Memory (LSTM)
      • Gated Recurrent Unit (GRU)

      Hyperparameter Tuning

      • Grid Search
      • Random Search

      Transfer Learning

      • Fine-tuning pre-trained models
      • Feature extraction

      AutoML

      • Automated model selection
      • Hyperparameter optimization

      Advanced Topics

      Advanced Deep Learning Architectures

      • Attention Mechanisms
      • Transformer Networks (BERT, GPT)

      Reinforcement Learning

      • Markov Decision Processes (MDPs)
      • Q-Learning

      Meta-Learning

      • Model-Agnostic Meta-Learning (MAML)
      • Learning to Learn

      Explainable AI (XAI)

      • Local and global interpretability
      • Model-specific and model-agnostic techniques

      Anomaly Detection

      • Statistical methods (Z-score, Grubbs' test)
      • Machine learning approaches (Isolation Forest, One-Class SVM)

        • Graph Neural Networks (GNNs)

        • Graph Convolutional Networks (GCNs)
        • Graph Attention Networks (GATs)

        Federated Learning

        • Decentralized model training
        • Privacy-preserving techniques (Differential Privacy)

        Quantum Machine Learning

        • Quantum circuits and gates
        • Quantum-inspired algorithms (Quantum Neural Networks, Quantum Support Vector Machines)

        Advanced NLP

        • Transformer-based architectures (BERT, GPT, T5)
        • Named Entity Recognition (NER)
        • Sentiment Analysis

        AI Ethics and Fairness

        • Bias and fairness in AI systems
        • Ethical considerations in data collection and model deployment

AI & ML Full Course Video