ML Engineering Course in Mysore Overview


Machine Learning (ML) engineering is a specialized field within software engineering focused on designing, developing, and deploying machine learning models and systems. It blends computer science, data science, and engineering principles to create scalable and effective ML solutions.

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