Machine Learning using Apache Spark MLlib | PySpark Tutorial
In this lecture, we're going to discuss about building Machine Learning models using MLlib, which is machine learning library in Apache Spark. We’ll first start with a brief introduction to machine learning, then cover best practices for distributed ML and feature engineering at scale.
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Anaconda Distributions Installation link:
https://www.anaconda.com/products/dis...
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PySpark installation steps on MAC: https://sparkbyexamples.com/pyspark/h...
Apache Spark Installation links:
1. Download JDK: https://www.oracle.com/in/java/techno...
2. Download Python: https://www.python.org/downloads/
3. Download Spark: https://spark.apache.org/downloads.html
Environment Variables:
HADOOP_HOME- C:\hadoop
JAVA_HOME- C:\java\jdk
SPARK_HOME- C:\spark\spark-3.3.1-bin-hadoop2
PYTHONPATH- %SPARK_HOME%\python;%SPARK_HOME%\python\lib\py4j-0.10.9-src;%PYTHONPATH%
Required Paths:
%SPARK_HOME%\bin
%HADOOP_HOME%\bin
%JAVA_HOME%\bin
Also check out our full Apache Hadoop course:
• Big Data Hadoop Full Course
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Apache Spark Installation links:
1. Download JDK: https://www.oracle.com/in/java/techno...
2. Download Python: https://www.python.org/downloads/
3. Download Spark: https://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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