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.
About this Course
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.
- 5 stars77.27%
- 4 stars14.70%
- 3 stars4.01%
- 2 stars1.60%
- 1 star2.40%
TOP REVIEWS FROM ADVANCED DATA SCIENCE CAPSTONE
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
This course will give a complete knowledge on different things involved in Data science.
Making my own data science project was a fun and rewarding project.
This was quite enriching as I was able to perform data science analysis with the help of Pyspark and Tensorflow
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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