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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.
Course Cost
₹ 2,699
Intermediate
Skill Level
6 Hours
Self-paced lessons
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.
23,083 Enrolled
English
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
Optimize machine learning workflows for scalability and performance
Gain practical experience using IBM's Apache Spark cluster for hands-on exercises
Skills you'll gain
This course includes:
2.45 Hours PreRecorded video
11 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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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
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
3.7
188
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7,03,752
Students
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Frequently asked Questions
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