IBM
Scalable Machine Learning on Big Data using Apache Spark

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IBM

Scalable Machine Learning on Big Data using Apache Spark

Romeo Kienzler

Instructor: Romeo Kienzler

23,189 already enrolled

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
3.8

(1,248 reviews)

Intermediate level
Some related experience required
Flexible schedule
Approx. 6 hours
Learn at your own pace
73%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
3.8

(1,248 reviews)

Intermediate level
Some related experience required
Flexible schedule
Approx. 6 hours
Learn at your own pace
73%
Most learners liked this course

Details to know

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Assessments

11 assignments

Taught in English

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Build your Machine Learning expertise

This course is part of the IBM AI Engineering Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
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There are 4 modules in this course

This is an introduction to Apache Spark. You'll learn how Apache Spark internally works and how to use it for data processing. RDD, the low level API is introduced in conjunction with parallel programming / functional programming. Then, different types of data storage solutions are contrasted. Finally, Apache Spark SQL and the optimizer Tungsten and Catalyst are explained.

What's included

6 videos6 readings2 assignments

Applying basic statistical calculations using the Apache Spark RDD API in order to experience how parallelization in Apache Spark works

What's included

8 videos3 readings4 assignments

Understand the concept of machine learning pipelines in order to understand how Apache SparkML works programmatically

What's included

5 videos2 readings3 assignments

Apply Supervised and Unsupervised Machine Learning tasks using SparkML

What's included

4 videos2 readings2 assignments

Instructor

Instructor ratings
3.7 (188 ratings)
Romeo Kienzler
IBM
10 Courses700,674 learners

Offered by

IBM

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3.8

1,248 reviews

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    24.73%

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  • 1 star

    8.80%

JS
5

Reviewed on Feb 25, 2020

SS
4

Reviewed on Feb 22, 2020

GG
4

Reviewed on May 19, 2020

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