Join us in this concise and insightful video as we explore the crucial steps of evaluating PySpark machine learning models and optimizing their performance through hyperparameter tuning. Learn essential evaluation metrics and techniques, gain hands-on experience with PySpark's MLlib library, and discover how to select and tune hyperparameters effectively. Whether you're a data scientist or a PySpark user, this guide will help you maximize the performance of your machine learning models. Don't miss out-watch now and take your skills to the next level!
Code for this lecture: github.com/ashaypatil11/spark...
Data file used in the lecture: github.com/ashaypatil11/spark...
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Anaconda Distributions Installation link:
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Apache Spark Installation links:
1. Download JDK: www.oracle.com/in/java/techno...
2. Download Python: www.python.org/downloads/
3. Download Spark: spark.apache.org/downloads.html
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Audience
This tutorial has been prepared for professionals/students aspiring to learn deep knowledge of Big Data Analytics using Apache Spark and become a Spark Developer and Data Engineer roles. In addition, it would be useful for Analytics Professionals and ETL developers as well.
Prerequisites
Before proceeding with this full course, it is good to have prior exposure to Python programming, database concepts, and any of the Linux operating system flavors.
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Негізгі бет Evaluating PySpark Machine Learning Model | Hyperparameter Tuning
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