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Learner Reviews & Feedback for Advanced Machine Learning and Signal Processing by IBM

1,167 ratings
212 reviews

About the Course

>>> 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 <<< This course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks. We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel. We’ll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms. For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link

Top reviews

Apr 28, 2020

I learned a bit in terms of signal processing and the theory behind that. That could have been a course by itself, but the addition of great machine learning material made it a wonderful experience.

Sep 7, 2018

A career changer course, thanks the hand-ons which is second to none, i have gained experience which on other online course can produce, thanks to IBM for this course which timely and excellent.

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101 - 125 of 209 Reviews for Advanced Machine Learning and Signal Processing

By 13_Soumyajit D

Oct 1, 2020

Very good , awesome

By Marvin L

Apr 10, 2020

it was educational

By Mukul K M

Jun 13, 2020

excellent course

By Madan T

Dec 10, 2019

Excellent course

By Saman S

Oct 26, 2019

that's wonderful

By Dr P R K

Oct 18, 2019

very informative

By Yuliia H

Jun 18, 2021

G​reat course!

By Alexander L

May 8, 2021

Very cool job!


Dec 25, 2019

Great course!

By Suhas J

Sep 20, 2020

great course

By Warren P

May 9, 2020

Great class!

By Hadhrami A G

May 6, 2020

Very Good


Jun 22, 2020

too good

By Jeff D

Jan 24, 2021


By Jérémie B

Feb 3, 2020


By Bikash R

Nov 21, 2019


By Ankit M

Dec 16, 2019


By Nyam-Ochir B

Nov 5, 2019


By Avijit P

Jul 8, 2020

The difficulty level of the course is as it states, intermediate.

Both the instructors are quite good at explaining things and also provide a little insight as to why they're choosing to do something at any given moment.

There is this one lecture though from a guest faculty that just plain reads out what's written in the presentation slides.

Although they try to explain every short thing, it might go over one's head or require repetition if the reader is 2 or 3 viewings with the mathematical concepts behind the algorithms previously. But the course still felt pretty self-contained to me,

Still, it's an overall balanced course that can't be completed unless one understands what the code is doing. Great for getting insights on and developing data science intuition.

By Humberto D

Jun 21, 2021

T​he instructors of this course are very good about explaining the intuition and the code. However, I must admit that the treatment of the mathematical content was superficial; there was not enough time devoted to mathematical formulations of the problems and their solutions. In fact, it is such that one should already be familiar with the mathematics of data science in order to understand what goes on under the hood during the coding implementations. Otherwise, one does not get a sense for why the techniques that are used work the way they do.

By Scott B

May 4, 2020

The information in the videos is excellent. I am actually very please by how succinct and clear the topics had been covered. My reason for giving 4 stars is because the programming assignments do not really help crystallize the new material. They may include a fraction of the concepts that are covered. It would be nice if the assignments involve stuff like the inclusion of param grids, comparing different ML algorithms, implementing PCA, etc. Also would be nice if there had been a review of the Fourier Transform material using SparkML.

By Alexander B

Nov 7, 2019

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 Taresh B

Jul 5, 2020

I like the course but I feel that it really needs more depth. It feels like most topics have been just skimmed through and not explained very well. The IBM tag is something that attracts you but if you wanna delve into the details, this course will tell you what to learn, and then you'll have to go on youtube and look for resources.

By Rishiraj A

Jul 8, 2020

I liked the course.

I like Week 4 of Advanced ML course. It is very fulfilling.

But, I think the portion of large data handling using parquet and spark is still missing in both the course (Scalable DS and Advance ML). There should be a session where is taught how to create parquet files and how to store them in object storage.

By Euripedes B d C N

May 19, 2019

O Curso é ótimo e apresenta muitos conceitos de Machine Learning e Processamento de sinais, mas faço uma ressalva, pois como o próprio nome diz é Avançado e o candidato precisa ter uma boa base de programação, particularmente precisei pesquisar bastante sobre Apache Spark e Systemml pois minha formação não é de TI.