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Avis et commentaires pour d'étudiants pour Advanced Machine Learning and Signal Processing par IBM

1,174 évaluations
213 avis

À propos du cours

>>> 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

Meilleurs avis

28 avr. 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.

7 sept. 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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176 - 200 sur 210 Avis pour Advanced Machine Learning and Signal Processing

par Mohsen F

24 déc. 2020

It is supposed to be an advanced course but there is almost no advanced topic in this course and it is just a shallow overview of machine learning with the Pyspark. SystemML explanation was very vague and incomplete.

par Björn ' H

19 sept. 2019

The assignments are too easy, the level of coding required is not very challenging, it's just a fill-in-the blanks exercise, I don't know if I could actually do any of these things on my own with a new data set.

par Michael P C

5 nov. 2020

Excellent brief math lectures by Manchev. The course materials do MOST of the programming for you and so you only get a light exposure to the Apache Spark API -- insufficient to develop real proficiency.

par Greg R

12 mai 2020

Overall very useful material covered however I was disappointed that some key concepts such as Baynesian inference and PCA were not well explained. I supplemented most of that material from Youtube.

par Mario R

8 août 2019

The learner needs to do more by his own. I think the course should follow up on the teaching style from the IBM specialization of Data Science. The teachers are good at replies.

par Borvorntat N

7 juil. 2020

Assignments are too easy, and not cover every lecture that I have learned for advanced ML, also some of the lectures are quite short.

par Mohammed E

29 mars 2021

The weekly assignments are quite bad, and the course name ,advanced ML and signal processing, is misleading

par Anastasiia S

15 sept. 2019

Not enough programming assignments and the ones in this course are too easy for the "advanced" course

par Salvatore S

11 janv. 2020

The assignments are way too easy. Not very challenging for a course with 'advanced' in its title.

par Thiago d S B

7 juil. 2020

Some videos with low quality so it was hard to read the code and lack of pratical exercicies

par Ayushman S

18 juil. 2020

The course might need some updating, it does give a lot of information about many things.

par Venkaatesh D

6 avr. 2021

Not too sure about the application of digital signal processing in real world problems

par Riku S

7 févr. 2019

A tad too much IoT for my professional interests (was part of larger "Specialization")

par Nima

8 juin 2020

Some explanations were good but that was not enough for covering machine learning

par Mark B

17 avr. 2020

Hard to follow at times... found a lot of assistance in discussion forum

par Prashant B

29 août 2019

The spark usage is very limited. Assignments could be more challenging.

par Nicolas M

17 avr. 2020

It should be useful to introduce more practical exercises

par Markus W

23 sept. 2019

well explained, programming assignments are worthless.

par Santiago M L

28 juin 2020

It's a little bit comlicated develop the activitites

par Rama K R

3 mai 2020

assignments should be more challenging

par Mattia S

22 mai 2020

I honestly expected so much better from IBM.

The idea of the course is great, it covers interesting topics about distributed machine learning, how to perform it with huge amount of data and how to solve scalability problems.

The idea is great, but it's really poorly executed. The course lacks of a real structure, connection between lessons and real explanations. The only lesson well structured and well organized are the ones held by Prof. Nicolay Manchev, they're clear, well structured and splitted into theory + practice.

The lesson about SystemMl are held by another professor (not mentioned in the instructor list) and they're almost impossible to understand, and i'm not talking about the content itself (which is still pretty poorly explained) but the pronunciation, it's really really hard to understand what he's saying.

Another real problem about this course is that seems more "marketing" focused that "learning" focused. Having followed also the previous course of the specialization, you can clearly tell that some explanation are more marketing than a real explanation. I understand the point of view of IBM, it's a company, they're interested in making money and marketing, and there's nothing wrong with it. But if a student finds himself annoyed because of this, it starts to be a problem. I don't care about your amazing offers ecc... i want to learn how to use and when to use those tool. Eventually i'll start using on my own your platform, i don't need constant remainders about how beautiful it is.

Last thing last, right now seems that IBM decided to completely remove the free basic plan on their platform (happened just today, and i had some problems finishing my last programming assignment). Doing so, they literally removed the possibility to test and learn on their platform, since you're limited by the monthly credits they give you.

This is pretty funny because the goal of the course (beside teching to students) is to promote IBM and their platform, and after this course and the removal of the free envirorment from Watson studio, i completely moved to Google and Kaggle, pretty ironic.

par Xavier U

3 mai 2020

I was initially excited about pyspark and SystemML but It's a very gentle introduction.

The assignements were way too simple machine learning wise. On one of them you just had to call the classifier. One word + (). Really?!

On the positive side, PCA, FFT and Wavelets were very well explained.

par Dmitry S

10 mars 2020

Quite an unbalanced course. Some material is very primitive, other is quite complex compared to the rest. Lab assignments could have been more elaborate. Even though I learned quite a bit, my expectations were higher for an advanced course offered by IBM

par Jukka A

16 févr. 2019

Course was hard to complete due to the version problems. Instructors should update material so that the course can be done with newest versions of programs.

par Andrea P

17 mai 2020

I haven't got any time for doing the course, as I joined the course 1 week ago.