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Advanced Data Science with IBM

Romeo Kienzler, Max Pumperla, Ilja Rasin, Niketan Pansare, Tom Hanlon, and Nikolay Manchev

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.

If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

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What's inside

Four courses

Fundamentals of Scalable Data Science

Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models.

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Advanced Machine Learning and Signal Processing

This course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization. You'll learn about supervised and unsupervised machine learning models, linear algebra, and popular machine learning frameworks like Scikit-Learn and SparkML. You'll also learn how to tune models in parallel and use a real-life example from IoT to exemplify the different algorithms.

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Applied AI with DeepLearning

(0 hours)

This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.

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Advanced Data Science Capstone

This project completer has proven a deep understanding of massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning. They can apply this knowledge in a real-world practical use case, justifying architectural decisions and understanding the characteristics of different algorithms, frameworks, and technologies and how they impact model performance and scalability.

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Time

16 hours

Level

Advanced

Via

Coursera

Institution

IBM

Instructors

Romeo Kienzler

Max Pumperla

Ilja Rasin

Niketan Pansare

Tom Hanlon

Nikolay Manchev

Level

Advanced

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