CourseraMachine LearningFree courseCertificate

Advanced Machine Learning and Signal Processing

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 th

4.1

Course Description

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<

Time: 16h

Instructor Details

Romeo Kienzler

4.1 Rating
69 Reviews

Romeo Kienzler holds a M. Sc. (ETH) in Information Systems, informatics & Applied Statistics (Swiss Federal Institute of Technology). He has nearly two decades of experience in Software Enineering, Database Administration and Information Integration. Since 2012 he works as a Data Scientist for IBM. He published several works in the field with international publishers and on conferences. His current research focus is on massive parallel data processing architectures. Romeo also contributes to various open source projects.

Fundamentals of Scalable Data Science
3.9 (161 Reviews)

- Provider: Coursera
- Time: 12h

Free

Applied AI with DeepLearning
4.0 (82 Reviews)

- Provider: Coursera
- Time: 18h

Free

Scalable Machine Learning on Big Data using Apache Spark
3.2 (15 Reviews)

- Provider: Coursera
- Time: 10h

Free

All courses by Romeo Kienzler

More Machine Learning courses

Machine Learning by Stanford (2011)
4.9 (943 Reviews)

- Provider: Coursera
- Time: 19h 30m

Free

Neural Networks and Deep Learning (2017)
4.8 (780 Reviews)

- Provider: Coursera
- Time: 7h

Free

Automated Machine Learning with AutoGluon Library in Python (2023)
4.8 (157 Reviews)

- Provider: Udemy
- Time: 5.5h

$84.99

Complete A.I. & Machine Learning, Data Science Bootcamp (2026)
4.7 (30,982 Reviews)

- Provider: Udemy
- Time: 44h

$129.99

Bayesian Machine Learning in Python: A/B Testing (2026)
4.7 (8,053 Reviews)

- Provider: Udemy
- Time: 10.4h

$109.99

[2026] Machine Learning: Natural Language Processing (V2)
4.7 (7,251 Reviews)

- Provider: Udemy
- Time: 23.5h

$99.99

All Machine Learning courses

Reviews

4.1

69 reviews in total

Select a bar to show only those reviews.Select the bar again to show every rating.

Write a review

By 李晨曦 on 16-Nov-19

Great course. Finally after learning Transformation methods like Fourier and Wavelet, I finally got to learn real life problem solving capabilities of them. Learned a lot!!!!!

By Alexander B on 7-Nov-19

Overall a decent course. The lecturers could go into more depth with some of the topics they covered to allow the learners to really grasp the concepts. I felt all of the assignments were too simple, possibly allowing you to pass even if you don't completely understand the material. More depth in the lectures and challenging assignments would leave me completely satisfied.

By N B on 5-Nov-19

nice

By Eleni K on 2-Nov-19

Quite different from the previous course! Much better, with more thorough explanations and hands on experience.

By Saman S on 26-Oct-19

that's wonderful

By Debakanta G on 24-Oct-19

Awesome experience of learning this coursera

By yash k on 21-Oct-19

Amazing course with real life usecase. A bit more explaination would have helped as most of the content is based on the fact that the viewers are familiar with SparkML/ SystemML

By Pratyush A on 18-Oct-19

IBM Watson studio can be made more user friendly.

By PV R K on 18-Oct-19

very informative

By Filip G on 27-Sep-19

This course is second in the IBM specialization. It covers basic supervised and unsupervised ML models on a very high level with too little explanations. Especially around veryfing results and optimizing models. Metrics, crossvalidation and gridsearch are all explained on cca. 10 minutes! On top I can't figure out why did the authors put in a whole week on Fourier Transformation.. :S

By Markus W on 24-Sep-19