Big Data with PySpark Crash Course | Machine Learning, Feature Engineering and More

Unlock the power of Big Data with PySpark ⚡ In this full crash course, you’ll master Apache Spark using Python and build scalable data workflows for real-world applications. From data cleaning to feature engineering and machine learning, this hands-on tutorial equips you with the skills needed to tackle massive datasets with confidence. Whether you're stepping into the world of distributed computing or sharpening your big data chops, this is your go-to PySpark guide.

In this tutorial, you’ll learn:
- How to process large datasets using Apache Spark’s Python API (PySpark).
- How to clean and transform real-world data at scale.
- How to engineer features for downstream machine learning tasks.
- How to implement and evaluate ML models using Spark MLlib.
- How to build a scalable recommendation engine using collaborative filtering.

🧠 What You’ll Learn in This Video:
- Introduction to PySpark: Learn Spark’s core architecture, use RDDs and DataFrames, and query data using PySpark SQL.
- Big Data Fundamentals: Understand the essentials of big data processing and explore datasets like Shakespeare’s works, FIFA 2018 stats, and genomic data.
- Data Cleaning with PySpark: Handle messy, large-scale data with practical tips for performance and maintainability.
- Feature Engineering at Scale: Use PySpark to wrangle data and create meaningful features for modeling.
- Machine Learning with PySpark: Implement ML pipelines with linear and logistic regression models, analyzing large datasets like flight delays and spam texts.
- Building Recommendation Systems: Create collaborative filtering models using the ALS algorithm with MovieLens and Million Songs datasets.

📕 Video Highlights
00:00:00 – Introduction & Course Overview
00:18:00 – Setting Up PySpark Environment
00:36:00 – Spark Architecture & SparkSession
00:54:00 – Introduction to RDDs
01:12:00 – DataFrames & Datasets Basics
01:30:00 – Data Ingestion: Reading Data (CSV, JSON, Parquet)
01:48:00 – DataFrame Transformations & Actions
02:06:00 – Column Operations & Expressions
02:24:00 – Filtering, Sorting & Selecting Data
02:42:00 – Aggregations & GroupBy Operations
03:00:00 – Joins & Union Operations
03:18:00 – User-Defined Functions (UDFs) & Pandas UDFs
03:36:00 – Spark SQL & Temporary Views
03:54:00 – Window Functions & Advanced Aggregations
04:12:00 – Handling Missing & Corrupted Data
04:30:00 – Performance Tuning: Caching & Persistence
04:48:00 – Partitioning & Data Skew
05:06:00 – Machine Learning with MLlib
05:24:00 – Structured Streaming Basics
05:42:00 – Advanced Topics & Course Conclusion

🖇️ Resources & Documentation

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