PySpark and Machine Learning Training Course

PySpark & Machine Learning 

Module 1: Big Data & Spark Foundations

• An overview of the Big Data ecosystem and Spark's role in modern data platforms
• Comprehending Spark architecture: drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Distinctions between RDD and DataFrame APIs and guidelines for selecting the appropriate approach
• Establishing and configuring SparkSession alongside fundamental principles of application configuration

Module 2: PySpark DataFrames

• Ingesting and exporting data from enterprise sources and formats (CSV, JSON, Parquet, Delta)
• Manipulating PySpark DataFrames: executing transformations and actions, managing column expressions, filtering, joining, and aggregating
• Executing advanced operations such as window functions, timestamp handling, and working with nested data structures
• Implementing data quality checks and writing maintainable, reusable PySpark code

Module 3: Processing Large Datasets Efficiently

• Grasping performance fundamentals: partitioning strategies, shuffle mechanics, caching, and persistence
• Utilizing optimization techniques such as broadcast joins and execution plan analysis
• Techniques for efficiently processing large datasets and adhering to best practices for scalable data workflows
• Understanding schema evolution and modern storage formats utilized in enterprise environments

Module 4: Feature Engineering at Scale

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

Module 5: Machine Learning with Spark MLlib

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

Module 6: End-to-End ML Pipelines

• Constructing comprehensive Machine Learning pipelines that integrate preprocessing, feature engineering, and modeling
• Applying strategies for splitting data into training, validation, and test sets
• Executing cross-validation and hyperparameter tuning using grid search and random search methods
• Organizing reproducible Machine Learning experiments

Module 7: Model Evaluation & Practical ML Decision Making

• Selecting and applying appropriate evaluation metrics for regression and classification tasks
• Detecting overfitting and underfitting to make informed practical decisions regarding model selection
• Interpreting feature importance and gaining insights into model behavior

Module 8: Production & Enterprise Practices

• Saving and loading models within Spark
• Implementing batch inference workflows for large datasets
• Gaining an understanding of the Machine Learning lifecycle in enterprise contexts
• Introduction to versioning, experiment tracking concepts, and fundamental testing strategies

Practical Outcome

• Competence in working independently with PySpark
• Skill in efficiently processing large datasets
• Capability to perform feature engineering at scale
• Ability to construct scalable Machine Learning pipelines