Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
This course is part of the Big Data Specialization
Offered By
About this Course
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessSkills you will gain
- Machine Learning Concepts
- Knime
- Machine Learning
- Apache Spark
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Welcome
Introduction to Machine Learning with Big Data
Data Exploration
Data Preparation
Classification
Evaluation of Machine Learning Models
Reviews
- 5 stars70.32%
- 4 stars23.77%
- 3 stars4.12%
- 2 stars1.03%
- 1 star0.74%
TOP REVIEWS FROM MACHINE LEARNING WITH BIG DATA
Great overview about the machine learning in general. There are still lots not covered specially the Neural Network algorithms. Learning Spark MLlib was great advantage of this course.
This has given me foundational knowledge in Machine Learning for someone who is not a practitioner. Thank you Mai Nguyen for the excellent course presentation. Keep it up.
Reasonable overview. The VM environment is a major challenge for my hardware. Takes more time to make it work than it should. I am wondering if a cloud solution e.g. GCP would be better.
The fact that the assignments are graded means that there’s incentive to work on them, solve problems, and ask questions. Traditional online courses don’t offer that incentive.
About the Big Data Specialization

Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.