Big Data with PySpark Crash Course | Machine Learning, Feature Engineering and More скачать в хорошем качестве

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 Take this skill track on DataCamp: https://www.datacamp.com/tracks/big-d... Introduction to PySpark – https://www.datacamp.com/courses/intr... Big Data Fundamentals with PySpark – https://www.datacamp.com/courses/big-... Cleaning Data with PySpark – https://www.datacamp.com/courses/clea... Feature Engineering with PySpark – https://www.datacamp.com/courses/feat... Machine Learning with PySpark – https://www.datacamp.com/courses/mach... Building Recommendation Engines with PySpark – https://www.datacamp.com/courses/buil... 📱 Follow Us on Social Facebook:   / datacampinc   Twitter:   / datacamp   LinkedIn:   / datacampinc   Instagram:   / datacamp   #PySpark #BigData #MachineLearning #DataEngineering #ApacheSpark #MLlib #RecommendationEngine #FeatureEngineering #DataCleaning #DataScience #DataCamp