Scalable Machine Learning on Big Data using Apache Spark
Master machine learning techniques for big data using Apache Spark, from data processing to advanced ML algorithms implementation.
Master machine learning techniques for big data using Apache Spark, from data processing to advanced ML algorithms implementation.
This course teaches scalable machine learning techniques for big data using Apache Spark. Students will learn to leverage cluster computing and distributed storage to process extremely large datasets efficiently. The curriculum covers Apache Spark fundamentals, including RDD and DataFrame APIs, and progresses to implementing machine learning algorithms using SparkML. Learners will gain hands-on experience with statistical calculations, dimensionality reduction, clustering, and supervised learning models on big data. The course emphasizes practical skills in building and optimizing machine learning pipelines for large-scale data processing and analysis.
3.8
(1,248 ratings)
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English
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What you'll learn
Understand Apache Spark's architecture and internal workings for big data processing
Implement parallel data processing strategies using RDD and DataFrame APIs
Apply statistical calculations and dimensionality reduction techniques on large datasets
Develop and optimize machine learning pipelines using SparkML
Implement clustering algorithms like K-means on big data
Build and evaluate supervised learning models such as linear and logistic regression
Skills you'll gain
This course includes:
2.45 Hours PreRecorded video
11 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 4 modules in this course
This course provides a comprehensive introduction to scalable machine learning using Apache Spark for big data applications. Students will learn the fundamentals of Apache Spark, including its internal workings and APIs like RDD and DataFrame. The curriculum covers parallel data processing strategies, functional programming basics, and the use of SparkSQL. Learners will gain hands-on experience in applying statistical calculations, dimensionality reduction techniques like PCA, and machine learning algorithms such as clustering and regression on large datasets. The course emphasizes the use of SparkML pipelines for efficient data processing and model building. By the end of the course, students will be able to implement both supervised and unsupervised learning tasks on big data, and understand how to optimize machine learning workflows for scalability.
Week 1: Introduction
Module 1 · 2 Hours to complete
Week 2: Scaling Math for Statistics on Apache Spark
Module 2 · 1 Hours to complete
Week 3: Introduction to Apache SparkML
Module 3 · 1 Hours to complete
Week 4: Supervised and Unsupervised learning with SparkML
Module 4 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Chief Data Scientist at IBM Specializing in Data Science and Parallel Processing Architectures
Romeo Kienzler is the Chief Data Scientist and Course Lead at IBM, where he leverages nearly two decades of experience in software engineering, database administration, and information integration. He holds a Master of Science from the Swiss Federal Institute of Technology (ETH) in Information Systems, Bioinformatics, and Applied Statistics. Since joining IBM in 2012, Romeo has focused his research on massive parallel data processing architectures and has published numerous works in the field through international publishers and conferences. In addition to his professional contributions, he is actively involved in various open-source projects. On Coursera, he teaches several courses, including Deep Learning with Keras and TensorFlow, Introduction to Big Data with Spark and Hadoop, Scalable Machine Learning on Big Data using Apache Spark, and Tools for Data Science, all designed to equip learners with essential skills in data science and machine learning
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3.8 course rating
1,248 ratings
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