Unit 866: Machine Learning Fundamentals and Applications

1. Introduction to Machine Learning

• What is Machine Learning?
  • Definition and overview
  • Historical context and evolution
• Applications of Machine Learning
  • Industry use cases (healthcare, finance, marketing, etc.)
  • Real-world examples
• Types of Machine Learning Algorithms
  • Overview: supervised, unsupervised, reinforcement learning
• Differences between Supervised, Unsupervised, and Reinforcement Learning
  • Key characteristics and examples

2. Data Preprocessing

• Importance of Data Preprocessing in Machine Learning
• Data Cleaning
  • Handling missing values
  • Removing duplicates and noise
• Data Transformation
  • Normalization techniques (min-max scaling, z-score normalization)
  • Feature scaling
• Handling Categorical Data
  • Encoding techniques (one-hot encoding, label encoding)
• Feature Engineering Basics
  • Creating new features
  • Feature selection overview

3. Supervised Learning

• Concept of Supervised Learning
• Popular Algorithms and Their Applications
  • Linear Regression
    • Model formulation
    • Use cases
  • Logistic Regression
    • Binary classification
    • Interpretation of coefficients
  • Decision Trees
    • Tree structure and decision rules
    • Advantages and limitations
  • Support Vector Machines (SVM)
    • Margin maximization
    • Kernel tricks
  • Neural Networks (introductory overview)
    • Basic architecture
    • Activation functions

4. Unsupervised Learning

• Introduction to Unsupervised Learning
• Clustering Algorithms
  • K-means Clustering
    • Algorithm steps
    • Choosing number of clusters
  • Hierarchical Clustering
    • Agglomerative vs divisive
    • Dendrogram interpretation
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
    • Concept and mathematical background
    • Applications and visualization

5. Model Evaluation and Selection

• Importance of Model Evaluation
• Evaluation Metrics
  • For regression: MSE, RMSE, MAE
  • For classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC
• Cross-Validation Techniques
  • k-fold cross-validation
  • Leave-one-out cross-validation
• Hyperparameter Tuning
  • Grid search
  • Random search
  • Automated tuning tools (brief mention)

6. Neural Networks and Deep Learning

• Overview of Neural Networks
  • Neuron model
  • Layers: input, hidden, output
• Deep Learning Concepts
  • Difference between shallow and deep networks
  • Backpropagation and training
• Popular Architectures
  • Convolutional Neural Networks (CNNs)
    • Architecture and applications (image recognition)
  • Recurrent Neural Networks (RNNs)
    • Architecture and applications (sequence data)

7. Model Deployment and Monitoring

• Strategies for Model Deployment
  • Batch vs real-time deployment
  • Deployment platforms and tools
• Monitoring Model Performance
  • Detecting model drift
  • Performance metrics in production
• Retraining Models
  • When and how to retrain
  • Automation of retraining pipelines

8. Ethical Considerations in Machine Learning

• Importance of Ethics in Machine Learning
• Bias in Data and Models
  • Sources of bias
  • Examples and case studies
• Fairness and Accountability
  • Fairness definitions and metrics
  • Accountability frameworks
• Transparency and Explainability
  • Techniques to interpret models
  • Communicating model decisions
• Mitigating Biases
  • Data collection strategies
  • Algorithmic approaches

End of Unit Outline