What Is Programming? And How To Get Started
January 28, 2025
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Instructor: Romeo Kienzler
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This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer.
Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer. After completing this course, you will be able to: - gain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data - understand how parallel code is written, capable of running on thousands of CPUs. - make use of large scale compute clusters to apply machine learning algorithms on Petabytes of data using Apache SparkML Pipelines. - eliminate out-of-memory errors generated by traditional machine learning frameworks when data doesn’t fit in a computer's main memory - test thousands of different ML models in parallel to find the best performing one – a technique used by many successful Kagglers - (Optional) run SQL statements on very large data sets using Apache SparkSQL and the Apache Spark DataFrame API. Enrol now to learn the machine learning techniques for working with Big Data that have been successfully applied by companies like Alibaba, Apple, Amazon, Baidu, eBay, IBM, NASA, Samsung, SAP, TripAdvisor, Yahoo!, Zalando and many others. NOTE: You will practice running machine learning tasks hands-on on an Apache Spark cluster provided by IBM at no charge during the course which you can continue to use afterwards. Prerequisites: - basic python programming - basic machine learning (optional introduction videos are provided in this course as well) - basic SQL skills for optional content The following courses are recommended before taking this class (unless you already have the skills) https://www.coursera.org/learn/python-for-applied-data-science or similar https://www.coursera.org/learn/machine-learning-with-python or similar https://www.coursera.org/learn/sql-data-science for optional lectures
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.
6 videos6 readings2 assignments
Applying basic statistical calculations using the Apache Spark RDD API in order to experience how parallelization in Apache Spark works
8 videos3 readings4 assignments
Understand the concept of machine learning pipelines in order to understand how Apache SparkML works programmatically
5 videos2 readings3 assignments
Apply Supervised and Unsupervised Machine Learning tasks using SparkML
4 videos2 readings2 assignments
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University of California San Diego
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1,248 reviews
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Reviewed on Dec 11, 2019
Really really REALLY enjoyed this course! The instructor does a masterful job of going from simple examples and building up complexity in a very logical and thorough way.
Reviewed on Apr 3, 2020
Nice course with real details and opportunities to practice. We just need some more private study to cement skills learnt.
Reviewed on Feb 22, 2020
for the last assignment we should have got the opportunity to code in the notebook instead of just running it and reporting results.
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