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:
[ Ссылка ]

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PySpark installation steps on MAC: [ Ссылка ]

Apache Spark Installation links:

1. Download JDK: [ Ссылка ]

2. Download Python: [ Ссылка ]

3. Download Spark: [ Ссылка ]

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:
[ Ссылка ]

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Apache Spark Installation links:

1. Download JDK: [ Ссылка ]

2. Download Python: [ Ссылка ]

3. Download Spark: [ Ссылка ]
Also check out similar informative videos in the field of cloud computing:

What is Big Data: [ Ссылка ]

How Cloud Computing changed the world: [ Ссылка ]

What is Cloud? [ Ссылка ]

Top 10 facts about Cloud Computing that will blow your mind! [ Ссылка ]

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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