PySpark & Machine Learning Training Course

PySpark & Machine Learning

Module 1: Big Data & Spark Foundations

• Overview of the Big Data ecosystem and the role of Spark in modern data platforms for government
• Understanding Spark architecture: driver, executors, cluster manager, lazy evaluation, Directed Acyclic Graph (DAG), and execution planning
• Differences between Resilient Distributed Datasets (RDD) and DataFrame APIs and when to use each approach
• Creating and configuring SparkSession and understanding application configuration fundamentals for government

Module 2: PySpark DataFrames

• Reading and writing data from enterprise sources and formats (CSV, JSON, Parquet, Delta) for government
• Working with PySpark DataFrames: transformations, actions, column expressions, filtering, joins, and aggregations
• Implementing advanced operations such as window functions, handling timestamps, and working with nested data
• Applying data quality checks and writing reusable, maintainable PySpark code for government

Module 3: Processing Large Datasets Efficiently

• Understanding performance fundamentals: partitioning strategies, shuffle behavior, caching, and persistence
• Using optimization techniques including broadcast joins and execution plan analysis
• Efficient processing of large datasets and best practices for scalable data workflows for government
• Understanding schema evolution and modern storage formats used in enterprise environments for government

Module 4: Feature Engineering at Scale

• Performing feature engineering with Spark MLlib: handling missing values, encoding categorical variables, and feature scaling
• Designing reusable preprocessing steps and preparing datasets for Machine Learning pipelines for government
• Introduction to feature selection and handling imbalanced datasets for government

Module 5: Machine Learning with Spark MLlib

• Understanding MLlib architecture and the Estimator/Transformer pattern for government
• Training regression and classification models at scale (Linear Regression, Logistic Regression, Decision Trees, Random Forest) for government
• Comparing models and interpreting results in distributed Machine Learning workflows for government

Module 6: End-to-End ML Pipelines

• Building end-to-end Machine Learning pipelines combining preprocessing, feature engineering, and modeling for government
• Applying train/validation/test split strategies for government
• Performing cross-validation and hyperparameter tuning using grid search and random search for government
• Structuring reproducible Machine Learning experiments for government

Module 7: Model Evaluation & Practical ML Decision Making

• Applying appropriate evaluation metrics for regression and classification problems for government
• Identifying overfitting and underfitting and making practical model selection decisions for government
• Interpreting feature importance and understanding model behavior for government

Module 8: Production & Enterprise Practices

• Persisting and loading models in Spark for government
• Implementing batch inference workflows on large datasets for government
• Understanding the Machine Learning lifecycle in enterprise environments for government
• Introduction to versioning, experiment tracking concepts, and basic testing strategies for government

Practical Outcome

• Ability to work autonomously with PySpark for government
• Ability to process large datasets efficiently for government
• Ability to perform feature engineering at scale for government
• Ability to build scalable Machine Learning pipelines for government