COURSE OVERVIEW

════════
A Machine Learning (ML) Engineering course typically covers the design, building, productionization, optimization, operation, and maintenance of ML systems, equipping students with hands-on experience using tools and technologies essential for the role.

Linear Algebra:

  • Vectors and matrices
  • Eigenvalues and eigenvectors
  • Singular value decomposition (SVD)

    • Calculus:

  • Differentiation and integration
  • Optimization methods (gradient descent, Newton's method)

    • Probability and Statistics:

  • Probability distributions(Gaussian, Bernoulli, etc.)
  • Hypothesis testing and confidence intervals
  • Bayesian statistics

    • Python Programming:

  • Data structures (lists,dictionaries, tuples, sets)
  • Functions, modules, packages
  • Exception handling, file I/O

    • Introduction to Machine Learning:

  • Supervised learning,unsupervised learning,reinforcement learning
  • Model evaluation metrics(accuracy, precision, recall, F1-score)

    • Regression

  • Decision trees, random forests, support vector machines (SVM)

    • Clustering:

  • K-means clustering, hierarchical clustering

    • Ensemble Methods:

  • Bagging (Bootstrap Aggregating), Boosting (AdaBoost, Gradient Boosting)

    • Clustering:

  • K-means clustering, hierarchical clustering
  • Density-based clustering (DBSCAN)

    • Dimensionality Reduction:

  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

    • Neural Networks Basics:

  • Perceptrons, activation functions (sigmoid, ReLU, tanh)
  • Feedforward neural networks, backpropagation

    • Deep Learning Frameworks:

  • Tensor Flow: Tensors, building neural networks
  • PyTorch: Tensors, autograd, dynamic neural networks

    • Convolutional Neural Networks (CNNs):

  • Architecture, filters, pooling layers
  • Applications in computer vision

    • Recurrent Neural Networks (RNNs):

  • LSTM, GRU for sequence modeling
  • Applications in natural language processing

    • Data Preprocessing:

  • Feature scaling, normalization
  • Handling missing data, outliers

    • Feature Engineering:
  • Creating new features, feature selection techniques

    • Data Pipelines:

  • Building efficient data pipelines for ML workflows

    • Cross-validation:

  • K-fold cross-validation, stratified cross-validation

    • Hyperparameter Tuning:

  • Grid search, random search, Bayesian optimization

    • Model Performance Metrics:

  • ROC-AUC, confusion matrix, precision-recall curve

    • Model Deployment:

  • Containerization using Docker
  • Deploying models using Flask, FastAPI

    • Production Considerations:

  • Scalability, latency, monitoring model performance

    • Cloud Platforms:

  • AWS, Google Cloud Platform, Azure for deploying ML models

    • Bias and Fairness:

  • Identifying and mitigating biases in data and models

    • Transparency and Explainability:

  • Interpreting model predictions, model explainability techniques

    • Privacy and Security:

  • Data privacy concerns, secure handling of sensitive data