Cloud Academy Working With Distributed Machine Learning-stm

[Prohibitive]

CLOUD ACADEMY WORKING WITH DISTRIBUTED MACHINE LEARNING-STM
Size: 2.16 GB
Genre: eLearning | Language: English

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.:PROUDLY PRESENTS:.

Cloud Academy Working with Distributed Machine Learning

Release Date.: 05-09-2019
Type.: Bookware
Disks.: 24x50mb
Link.: https://cloudacademy.com

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Overview
This training course begins with an introduction to the
concepts of Distributed Machine Learning. We'll discuss the
reasons as to why and when you should consider training
your machine learning model within a distributed environment.

Apache Spark
WeÆll introduce you to Apache Spark and how it can be used
to perform machine learning both at scale and speed. Apache
Spark is an open-source cluster-computing framework.
Amazon Elastic Map Reduce
WeÆll introduce you to AmazonÆs Elastic MapReduce service,
or EMR for short. EMR provides a managed Hadoop framework
that makes it easy, fast, and cost-effective to process
vast amounts of data. EMR can be easily configured to host
Apache Spark.
Spark MLlib
WeÆll introduce you to MLlib which is SparkÆs machine
learning module. WeÆll discuss how MLlib can be used to
perform various machine learning tasks. For this course,
we'll focus our attention on decision trees as a machine
learning method which the MLlib module supports. A decision
tree is a type of supervised machine learning algorithm
used often for classification problems.
AWS Glue
WeÆll introduce you to AWS Glue. AWS Glue is a fully
managed extract, transform, and load service, ETL for
short. WeÆll show you how AWS Glue can be used to prepare
our datasets before they are used to train our machine
learning models.
Demonstration
Finally, weÆll show you how to use each of the
aforementioned services together to launch an EMR cluster
configured and pre-installed with Apache Spark for the
purpose of training a machine learning model using a
decision tree. This demonstration will provide an end-to-
end solution that provides machine learning predictive capabilities.
Intended Audience
The intended audience for this course includes:

Data scientists and/or data analysts
Anyone interested in learning and performing distributed
machine learning, or machine learning at scale
Anyone with an interest in Apache Spark and/or Amazon
Elastic MapReduce

Learning Objectives
By completing this course, you will:

Understand what Distributed machine learning is and what it offers
Understand the benefits of Apache Spark and Elastic MapReduce
Understand Spark MLlib as machine learning framework
Create your own distributed machine learning environment
consisting of Apache Spark, MLlib, and Elastic MapReduce.
Understand how to use AWS Glue to perform ETL on your
datasets in preparation for training a your machine
learning model
Know how to operate and execute a Zeppelin notebook,
resulting in job submission to your Spark cluster
Understand what a machine learning Decision Tree is and how
to code one using MLlib

Pre-requisites
The following prerequisites will be both useful and helpful
for this course:

A background in statistics or probability
Basic understanding of data analytics
General development and coding experience
AWS VPC networking and IAM security experienceá(for the demonstrations)

Course Agenda
The agenda for the remainder of this course is as follows:

WeÆll discuss what Distributed Machine Learning is and when
and why you might consider using it
WeÆll review the Apache Spark application, and its MLlib
machine learning module
WeÆll review the Elastic MapReduce service
WeÆll provide an understanding what a Decision Tree is -
and what types of analytical problems it is suited towards
WeÆll review the basics of using Apache Zeppelin notebooks -
which can be used for interactive machine learning sessions
WeÆll review AWS Glue. WeÆll show you how you can use AWS
Glue to perform ETL to prepare our datasets for ingestion
into a machine learning pipeline.
Finally - WeÆll present a demonstration of a fully
functional distributed machine learning environment
implemented using Spark running on top of an EMR cluster

Feedback
If you have thoughts or suggestions for this course, please
contact Cloud Academy atá[email protected].

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▀▓▓███▀ Greetings fly out to: ▀███▓▓▀
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▄▓▓█▓▓██▄ Kodemusen, KoseBamsen ▄██▓▓█▓▓▄
▄▓▓██ ▓▓██▄ ▄██▓▓ ██▓▓▄
▄▓▓██ ▓▓██▄ STM is back. ▄██▓▓ ██▓▓▄
▄▓▓██ ▓▓██▄ For all the ppl we worked with ▄██▓▓ ██▓▓▄
▄▓▓██▀ ▓▓██▄ in the past. We salute you. ▄██▓▓ ▀██▓▓▄
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▀▓▓█ ▐████▓▓▓▌ NFO by NiMiTech ▐▓▓▓████▌ █▓▓▀
▀▓▓█▄ ▐█████▓▓▌ Updated: 09/09/2002 ▐▓▓█████▌ ▄█▓▓▀
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