Organizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery and more to identify behaviors and preferences of prospects, clients, competitors, and others.
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IBMОб этом курсе
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Начальный уровень
Computer and IT literacy.
Прибл. 7 часов на выполнение
Английский
Субтитры: Английский
Чему вы научитесь
Glean insights into how streaming data and Spark Structured Streaming empower machine learning and AI tasks.
Delve into graph theory and Apache Spark GraphFrames, used for motif finding in genetics and biological sciences, and learn to identify data.
Discover how ETL processes work with Apache Spark and machine learning and extend that knowledge to Spark MLlib capabilities and related benefits.
Explore supervised learning and unsupervised learning, clustering, and learn how to use the k-means clustering algorithm with Spark MLlib.
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Начальный уровень
Computer and IT literacy.
Прибл. 7 часов на выполнение
Английский
Субтитры: Английский
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IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Программа курса: что вы изучите
2 ч. на завершение
Spark for Data Engineering
In this first of two modules, learn what streaming data is and get the essential knowledge to use Spark for Structured Streaming. Learn about data sources, streaming output modes, and supported data destinations. Learn about data operations considerations and discover how Spark Structured streaming listeners and checkpointing benefit streaming data processing. Discover how graph theory works with streaming data. You’ll gain insights into the advantages that Apache Spark GraphFrames offers and learn what qualities make data suitable for GraphFrames processing. Then, explore ETL and learn how to use Apache Spark for data extraction, transformation, and loading, put your newfound knowledge to practice, and gain practical, real-world skills in the ETL for Machine Learning Pipelines hands-on lab.
2 ч. на завершение
4 видео ((всего 25 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
2 ч. на завершение
SparkML
This module demystifies the concepts and practices related to machine learning using SparkML and the Spark Machine learning library. Explore both supervised and unsupervised machine learning. Explore classification and regression tasks and learn how SparkML supports these machine learning tasks. Gain insights into unsupervised learning, with a focus on clustering, and discover how to apply the k-means clustering algorithm using the Spark MLlib. Complete this learning with the lab that solidifies your learning and gain real-world experience with Spark ML.
2 ч. на завершение
3 видео ((всего 13 мин.)), 1 материал для самостоятельного изучения, 3 тестов
3 ч. на завершение
Final Project
This final project provides real-world experience where you'll create your own Apache Spark application. You will create this Spark application as an end-to-end use-case that follows the Extract, Transform and Load processes (ETL) including data acquisition, transformation, model training, and deployment using IBM Watson Machine Learning.
3 ч. на завершение
3 материалов для самостоятельного изучения
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