This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability.
This course is part of the Advanced Data Science with IBM Specialization
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Course 4 of 4 in the
Advanced Level
Approx. 9 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Russian, English, Spanish
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 4 in the
Advanced Level
Approx. 9 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Russian, English, Spanish
Offered by
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.
Syllabus - What you will learn from this course
2 hours to complete
Week 1 - Identify DataSet and UseCase
In this module, the basic process model used for this capstone project is introduced. Furthermore, the learner is required to identify a practical use case and data set
2 hours to complete
1 video (Total 2 min), 7 readings, 2 quizzes
2 hours to complete
Week 2 - ETL and Feature Creation
This module emphasizes on the importance of ETL, data cleansing and feature creation as a preliminary step in ever data science project
2 hours to complete
3 readings
2 hours to complete
Week 3 - Model Definition and Training
This module emphasizes on model selection based on use case and data set. It is important to understand how those two factors impact choice of a useful model algorithm.
2 hours to complete
2 readings
3 hours to complete
Model Evaluation, Tuning, Deployment and Documentation
One a model is trained it is important to assess its performance using an appropriate metric. In addition, once the model is finished, it has to be made consumable by business stakeholders in an appropriate way
3 hours to complete
5 readings
Reviews
TOP REVIEWS FROM ADVANCED DATA SCIENCE CAPSTONE
by KWJul 11, 2019
Innovative teacher and course. Learned how to make a youtube video beside the data science challenge. On the whole specialization, very comprehensive and set me up to want to delve further next.
by SSJan 13, 2019
I liked the peer-graded environment. Like the final submission requirements. That's really helps in aquiring the skills like presentation skills, Documentation skills, project mangement
by VTMar 17, 2019
Like that course. It combine all you skills in a one project. It is very helpful for the understanding why and how ML can help for the business. Personal thanks for Romeo Kienzler!
by MZJan 20, 2020
This is data crunching, varied machine learning model exploring, training, testing, validating and deployment course. What a goldmine for both novices and seasoned and initiated!
About the Advanced Data Science with IBM Specialization
As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.
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