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.
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%
来自ADVANCED DATA SCIENCE CAPSTONE的热门评论
This was quite enriching as I was able to perform data science analysis with the help of Pyspark and Tensorflow
It is a great course to prove how much you know about data science and machine learning
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!
Good course. But the peer-rating system could be improved: it takes a long period of time (several days) to get a review.
关于 Advanced Data Science with IBM 专项课程
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.