Dive into a comprehensive tutorial on PySpark, the Python interface for Apache Spark, designed for large-scale data processing and machine learning. Explore essential topics including PySpark introduction, working with DataFrames, handling missing values, groupby and aggregate functions, and MLlib implementation. Gain hands-on experience with Databricks and learn to implement Linear Regression using single clusters. Access accompanying code on GitHub and benefit from instructor Krish Naik's expertise throughout this 1-2 hour learning journey.
Overview
Syllabus
Pyspark Introduction.
Pyspark Dataframe Part 1.
Pyspark Handling Missing Values.
Pyspark Dataframe Part 2.
Pyspark Groupby And Aggregate Functions.
Pyspark Mlib And Installation And Implementation.
Introduction To Databricks.
Implementing Linear Regression using Databricks in Single Clusters.
Taught by
freeCodeCamp.org
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Reviews
5.0 rating, based on 1 Class Central review
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The PySpark tutorial course is an excellent choice for beginners due to its clear and step-by-step practical approach. The explanations provided are thorough, making it easy for newcomers to grasp the fundamental concepts of PySpark and Apache Spark. The course effectively introduces learners to Spark's powerful data processing capabilities and demonstrates how to apply them in real-world scenarios.
What sets this tutorial apart is its focus on practicality. Each concept is accompanied by hands-on exercises and examples that reinforce the learning process. The course guides learners through the installation and setup of PySpark, ensuring a smooth transition into using the framework.
