PySpark and Machine Learning Training Course

PySpark & Machine Learning 

Module 1: Big Data & Spark Fundamentals

• An overview of the Big Data landscape and Spark's role in modern data platforms
• Examining Spark architecture: drivers, executors, cluster managers, lazy evaluation, DAGs, and execution planning
• Distinguishing between RDD and DataFrame APIs and determining when to apply each approach
• Establishing and configuring SparkSession and grasping the basics of application settings

Module 2: PySpark DataFrames

• Importing and exporting data from enterprise sources and formats (CSV, JSON, Parquet, Delta)
• Utilizing PySpark DataFrames for transformations, actions, column expressions, filtering, joins, and aggregations
• Implementing advanced operations like window functions, timestamp handling, and navigating nested data structures
• Executing data quality validations and crafting reusable, maintainable PySpark code

Module 3: Efficient Processing of Large Datasets

• Grasping performance basics: partitioning strategies, shuffle behavior, caching, and persistence
• Applying optimization techniques such as broadcast joins and analyzing execution plans
• Processing large volumes of data efficiently and adhering to best practices for scalable workflows
• Understanding schema evolution and contemporary storage formats prevalent in enterprise environments

Module 4: Large-Scale Feature Engineering

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

Module 5: Machine Learning with Spark MLlib

• Understanding the MLlib architecture and the Estimator/Transformer paradigm
• Training regression and classification models at scale (Linear Regression, Logistic Regression, Decision Trees, Random Forest)
• Comparing model performance and interpreting outcomes in distributed Machine Learning contexts

Module 6: End-to-End ML Pipelines

• Constructing complete Machine Learning pipelines that integrate preprocessing, feature engineering, and modeling
• Implementing train/validation/test split methodologies
• Executing cross-validation and hyperparameter tuning using grid search and random search techniques
• Structuring reproducible Machine Learning experiments

Module 7: Model Evaluation & Practical ML Decision Making

• Selecting appropriate evaluation metrics for regression and classification tasks
• Detecting overfitting and underfitting to make informed model selection decisions
• 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
• Understanding the Machine Learning lifecycle in enterprise contexts
• Introduction to version control, experiment tracking concepts, and fundamental testing strategies

Practical Outcome

• Proficiency in working independently with PySpark
• Capability to process large datasets with high efficiency
• Skill in performing feature engineering at scale
• Ability to build scalable Machine Learning pipelines