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ML Engineering Bootcamp

1. Python for Machine Learning:
   • Python syntax and basics (variables, loops, conditionals)
   • Data structures (lists, dictionaries, sets, tuples)
   • File handling and working with CSV, JSON, and Excel
   • Writing reusable functions and classes (OOP concepts)
   • Python libraries (NumPy, Pandas, Matplotlib, Seaborn)
   • List comprehensions and generator expressions
   • Error handling (try-except blocks)
   • Working with Jupyter Notebooks and IDEs (PyCharm, VS Code)
   • Using virtual environments and package management (pip, conda)
   • Debugging and testing (PyTest, logging, breakpoints)
2. Data Preprocessing:
   • Handling missing data (mean, median, mode imputation, forward/backward fill)
   • Feature scaling (min-max scaling, standardization, normalization)
   • Categorical encoding (one-hot encoding, label encoding, binary encoding)
   • Feature selection (correlation matrix, recursive feature elimination)
   • Feature engineering (creating new features, feature transformation)
   • Dealing with outliers (Z-score, IQR method, capping/flooring)
   • Handling imbalanced datasets (SMOTE, undersampling, oversampling)
   • Binning continuous variables (equal-width, equal-frequency)
   • Data transformation (log transformation, Box-Cox, Yeo-Johnson)
   • Splitting data (train-test, cross-validation)
3. Exploratory Data Analysis (EDA):
   • Data visualization (bar plots, histograms, scatter plots, box plots)
   • Summary statistics (mean, median, mode, variance, standard deviation)
   • Correlation analysis (Pearson, Spearman correlation, heatmaps)
   • Pairwise relationships (pair plots, scatter matrix)
   • Identifying trends and patterns in data (time series analysis)
   • Detecting anomalies and outliers (box plot, z-score, IQR)
   • Visualizing distributions and data spread (kernel density plots)
   • Dimensionality reduction for visualization (PCA, t-SNE)
   • Handling skewed data (log transformations, power transformations)
   • Grouping and aggregating data for analysis (groupby, pivot tables)
4. Supervised Learning Algorithms:
   • Linear regression (assumptions, regularization with Lasso/Ridge)
   • Logistic regression (binary classification, ROC curves, AUC)
   • Decision trees (gini index, information gain, pruning)
   • Random forests (bagging, feature importance, OOB error)
   • Support vector machines (kernel trick, margin maximization)
   • Gradient boosting algorithms (XGBoost, LightGBM, CatBoost)
   • k-Nearest Neighbors (KNN) for classification and regression
   • Naive Bayes classifier (Gaussian, Multinomial, Bernoulli)
   • Model evaluation metrics (accuracy, precision, recall, F1 score)
   • Hyperparameter tuning (grid search, random search, Bayesian optimization)
5. FastAPI for ML Applications:
   • Introduction to FastAPI (asynchronous programming, Uvicorn server)
   • Building ML APIs with FastAPI (input validation, request handling)
   • Asynchronous endpoints for scalable ML services
   • Dependency injection for database and model loading
   • CORS and security settings for FastAPI apps
   • Model deployment with FastAPI and Docker
   • Creating background tasks for long-running ML jobs
   • API versioning and documentation with Swagger/OpenAPI
   • Integrating FastAPI with databases (SQLAlchemy, NoSQL)
   • Load testing FastAPI applications (Locust, Apache Bench)
6. Flask for ML Applications:
   • Introduction to Flask (routing, request handling, WSGI)
   • Building RESTful APIs with Flask for ML models
   • Model deployment using Flask and Docker
   • Handling file uploads (for ML model inputs) with Flask
   • Using Flask extensions for database integration (Flask-SQLAlchemy)
   • Securing Flask APIs with authentication (JWT, OAuth)
   • Error handling and logging in Flask
   • Creating modular applications with Flask Blueprints
   • Deploying Flask apps on cloud platforms (Heroku, AWS, Azure)
   • Integrating Flask with front-end technologies (Flask-SocketIO, Flask-CORS)
7. Unsupervised Learning Algorithms:
   • K-means clustering (elbow method, silhouette score)
   • Hierarchical clustering (agglomerative, divisive, dendrograms)
   • Principal Component Analysis (PCA) for dimensionality reduction
   • t-SNE and UMAP for visualization of high-dimensional data
   • DBSCAN for density-based clustering
   • Association rule learning (Apriori algorithm, FP-Growth)
   • Anomaly detection techniques (Isolation Forest, One-Class SVM)
   • Gaussian Mixture Models (GMMs) for clustering
   • Self-organizing maps (SOMs) for clustering and visualization
   • Autoencoders for unsupervised feature learning and anomaly detection
8. MLP Classifier and Neural Networks:
   • Structure of neural networks (input, hidden, output layers)
   • Activation functions (ReLU, Sigmoid, Tanh, Softmax)
   • Forward propagation and backpropagation
   • Cost functions (mean squared error, cross-entropy)
   • Optimizers (gradient descent, Adam, RMSProp)
   • Regularization techniques (dropout, weight decay)
   • Multi-class classification with MLP
   • Early stopping and learning rate schedules
   • Model initialization techniques (He, Xavier, uniform initialization)
   • Using TensorFlow and PyTorch for building neural networks
9. Optimization Techniques:
   • Gradient Descent (batch gradient descent, mini-batch SGD)
   • Stochastic Gradient Descent (SGD) with momentum
   • Adam, RMSProp, and AdaGrad optimizers
   • Learning rate schedules (step decay, exponential decay)
   • Adaptive learning rates with AdamW and Nadam
   • Early stopping to prevent overfitting
   • L1 and L2 regularization (Lasso and Ridge)
   • Optimizing memory usage for large datasets
   • Distributed training and parallelization techniques
   • Hyperparameter tuning with Random and Grid Search
10. Azure Virtual Machines for ML:
    • Creating and configuring Azure Virtual Machines for ML
    • Choosing the right VM size and instance types for ML workloads
    • Installing ML frameworks and libraries on VMs (TensorFlow, PyTorch)
    • Running Jupyter Notebooks on Azure VMs
    • Using Azure Bastion for secure VM access
    • Scaling VMs using Azure Virtual Machine Scale Sets
    • Setting up autoscaling for cost-efficient compute
    • Monitoring VM performance (CPU, RAM, disk usage)
    • Backup and recovery strategies for Azure VMs
    • Securing Azure VMs with firewalls, SSH keys, and managed identities
11. Azure Container Apps (ACA):
    • Introduction to Azure Container Apps for ML model deployment
    • Building and containerizing ML models with Docker
    • Pushing containers to Azure Container Registry (ACR)
    • Deploying containerized models on ACA
    • Autoscaling containers with Azure Container Apps
    • Monitoring and logging container performance
    • Using Azure Virtual Networks (VNet) with Container Apps
    • Securing containers with Azure IAM (Identity and Access Management)
    • Integrating ACA with Azure Functions and Logic Apps
    • Handling high availability and load balancing for containerized applications
12. Model Evaluation & Metrics:
    • Confusion matrix (True Positive, False Positive, False Negative, True Negative)
    • Accuracy, Precision, Recall, and F1 Score
    • ROC-AUC Curve and Area Under Curve (AUC)
    • Precision-Recall curves for imbalanced datasets
    • Cross-validation (K-fold, stratified, leave-one-out)
    • Bias-variance tradeoff and model overfitting/underfitting
    • Metrics for regression (Mean Absolute Error, RMSE, R-squared)
    • Class imbalance handling (SMOTE, undersampling, oversampling)
    • Model calibration and reliability curves
    • Model interpretability (SHAP, LIME)
13. Docker for ML:
    • Building Docker images for ML models
    • Writing Dockerfiles for ML applications
    • Managing Docker containers using Docker CLI and Docker Desktop
    • Container networking and volume management
    • Best practices for optimizing Docker images (multi-stage builds, caching)
    • Pushing images to Docker Hub and private registries
    • Running GPU-enabled Docker containers for ML
    • Automating Docker builds with GitHub Actions or Jenkins
    • Containerizing Jupyter Notebooks for reproducibility
    • Managing Docker containers in production environments
14. Docker Compose:
    • Writing docker-compose.yml for multi-container applications
    • Defining services, volumes, and networks in Docker Compose
    • Orchestrating ML pipelines using Docker Compose
    • Managing environment variables and secrets in Docker Compose
    • Scaling services using Docker Compose (replicas, load balancing)
    • Networking between containers (service discovery)
    • Persisting data with Docker volumes in Compose
    • Restart policies and service health checks in Docker Compose
    • Integrating Docker Compose with CI/CD pipelines
    • Using Docker Compose in development and production environments
15. Microservices Architecture:
    • Overview of microservices (decentralized architecture)
    • Building ML services as microservices (modularity, loose coupling)
    • Communication between microservices (REST, gRPC, messaging queues)
    • Designing scalable and fault-tolerant microservices
    • API gateway patterns for managing microservices traffic
    • Service discovery and load balancing in microservices architecture
    • Monitoring and logging in microservices (Prometheus, Grafana)
    • Fault tolerance strategies (circuit breaker, retry mechanisms)
    • Security in microservices (OAuth2, JWT, API keys)
    • Deploying microservices using Kubernetes and Docker Swarm
16. Hyperparameter Tuning:
    • Grid Search for hyperparameter tuning
    • Random Search for efficient hyperparameter exploration
    • Bayesian Optimization for hyperparameters
    • Cross-validation strategies for hyperparameter tuning
    • Early stopping to optimize tuning processes
    • Hyperparameter tuning for deep learning models
    • Automated machine learning (AutoML) for hyperparameter selection
    • Tuning learning rates, batch sizes, and optimizers
    • Hyperparameter tuning for tree-based models (XGBoost, LightGBM)
    • Tracking and managing hyperparameter experiments (MLflow)
17. Azure Machine Learning Service:
    • Setting up and managing Azure Machine Learning Workspaces
    • Training models on Azure ML using compute clusters
    • Using Azure ML Designer for visual machine learning pipelines
    • AutoML for automatic model selection and tuning
    • Versioning and tracking models with Azure ML
    • Deploying models as web services using Azure ML Endpoints
    • Monitoring deployed models for performance and drift
    • Integrating Azure ML with GitHub and Azure DevOps for MLOps
    • Managing datasets and datastores in Azure ML
    • Cost management and optimization strategies in Azure ML
18. MLOps:
    • Creating automated CI/CD pipelines for ML models
    • Versioning models and monitoring model drift
    • Automating model deployment and updates in production
    • Managing model artifacts with MLflow and DVC
    • Monitoring and logging model performance in production
    • A/B testing and shadow deployments for ML models
    • Scaling models in production using Kubernetes and Azure AKS
    • Ensuring reproducibility with containerized ML models (Docker)
    • Integrating ML pipelines with GitHub Actions, Jenkins, or Azure DevOps
    • Handling model governance and compliance (data privacy, security)
19. Time Series Analysis:
    • Time series decomposition (trend, seasonality, residual components)
    • Autoregressive models (AR, ARMA, ARIMA)
    • Exponential smoothing methods (Holt-Winters, SES)
    • LSTM networks for time series forecasting
    • Evaluation metrics for time series models (MAE, RMSE, MAPE)
    • Time series cross-validation and backtesting techniques
    • Seasonal decomposition of time series (STL, multiplicative models)
    • Handling seasonality and cyclic trends in time series data
    • Feature engineering for time series data (lag, window features)
    • Multi-step forecasting and handling long-range dependencies
20. Azure Data Factory:
    • Creating data pipelines with Azure Data Factory
    • Data integration and ETL processes using ADF
    • Connecting ADF to various data sources (Azure Blob, SQL, etc.)
    • Scheduling and orchestrating data workflows with ADF
    • Monitoring and managing pipeline execution in ADF
    • Handling data transformations with ADF (mapping data flows)
    • Using ADF for big data processing with Spark and Databricks
    • Implementing fault tolerance and retries in ADF pipelines
    • Security and access control in ADF (managed identities, data encryption)
    • Integrating ADF with other Azure services (Azure ML, Synapse Analytics)
21. Cloud Platforms for ML:
    • Comparison of Azure ML, AWS SageMaker, and Google AI Platform
    • Training and deploying models on Azure ML
    • Using cloud storage for datasets and models (Azure Blob, AWS S3)
    • Cost management and optimization for cloud ML services
    • Using cloud GPUs and TPUs for large-scale ML training
    • Distributed training and model parallelism on cloud platforms
    • Monitoring and managing cloud-based ML workloads
    • Serverless ML using AWS Lambda, Azure Functions, and Google Cloud Functions
    • Hybrid cloud and multi-cloud strategies for ML
    • Data security and compliance in cloud ML environments
22. GenAI and LLMs:
    • Introduction to large language models (GPT, BERT, T5, etc.)
    • Fine-tuning pre-trained LLMs for specific tasks
    • Transfer learning using LLMs for NLP applications
    • Prompt engineering and using LLM APIs (OpenAI, Hugging Face)
    • Deploying large models efficiently (model pruning, quantization)
    • Generative AI applications (text generation, summarization, translation)
    • Ethical considerations in using LLMs (bias, privacy, fairness)
    • Training custom language models with Transformer architectures
    • Zero-shot, few-shot learning with LLMs
    • Comparing LLMs with traditional NLP techniques