PySpark and Machine Learning Training Course

PySpark & Machine Learning 

Module 1: Big Data & Spark Foundations

• Overview of the Big Data ecosystem and Spark's role in contemporary data platforms
• Understanding Spark architecture: drivers, executors, cluster managers, lazy evaluation, DAG, and execution planning
• Distinctions between RDD and DataFrame APIs and when to utilise each approach
• Creating and configuring SparkSession, alongside fundamental application configuration

Module 2: PySpark DataFrames

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

Module 3: Processing Large Datasets Efficiently

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

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
• Introduction to feature selection and handling imbalanced datasets

Module 5: Machine Learning with Spark MLlib

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

Module 6: End-to-End ML Pipelines

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

Module 7: Model Evaluation & Practical ML Decision Making

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

Module 8: Production & Enterprise Practices

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

Practical Outcome

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