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Opiniones y comentarios de aprendices correspondientes a Advanced Machine Learning and Signal Processing por parte de IBM

1,167 calificaciones
212 reseña

Acerca del Curso

>>> 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

Principales reseñas

28 de abr. de 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 de sep. de 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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151 - 175 de 209 revisiones para Advanced Machine Learning and Signal Processing

por Vignesh

27 de jun. de 2020

A very intuitive way of learning and great to see a mixture of ML and Signal Processing

por Sameera P

16 de sep. de 2018

I like what's taught in the course but the questions assignments are too simple.

por Ahin B

15 de jun. de 2020

I have earned good knowledge on Machine learning along with python programming.

por Kuldeep S S

6 de may. de 2020

This Course is very good but programming explaination is not good.

por John M

14 de ene. de 2019

Great course overall. A few small wrinkles that need fixing.

por Pratyush A 1

18 de oct. de 2019

IBM Watson studio can be made more user friendly.

por Paul A

29 de sep. de 2021

Well explained topics with wonderful instructors

por Camilo A S B

11 de oct. de 2020

Good, I would like more programming explanation

por Estananto

1 de oct. de 2020

Great videos help me to understand the topics.

por Swarupa D

22 de may. de 2020

it's very helpful...Thank u Sir for guiding me

por Krishna K N

14 de abr. de 2020

Programming exercises could be more difficult.

por Pragati A

28 de jun. de 2020

Both of the instructors were really amazing.

por Jeffrey G D

15 de ene. de 2020

Great concepts, but light on application.


22 de nov. de 2019

Some spelling errors here and there

por Rich E

25 de feb. de 2020

Great explanations and examples

por 俊鴻 林

3 de dic. de 2019

Thank courser and teachers

por Aditya K

27 de jun. de 2019

Great learning!!!

por Megha S k

12 de sep. de 2020

Very Good Course

por Chris E

23 de mar. de 2021

A good summary of machine learning, but way too quick. Only skims the surface and I doubt anyone could come away from this course with a good understanding of the material. You will know how to write the required code in Apache Spark but fundamentals are limited, so developing and iterating your models outside of the code templates will be difficult. Likely to have a lot of people creating models that they have no idea about how they actually work, or even if they do work.

por Filip G

27 de sep. de 2019

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

por Stefan T

31 de dic. de 2019

I don't like giving negative reviews, but for the amount of money asked for the certification I would expect better quality of material (audio especially). I took many courses back in the day it was free to do and the quality of material was much much higher.

The course is well presented, but if you don't use IBM environment and their libraries, you will not be so happy to follow.

por Jeramie G

4 de sep. de 2019

The information and examples presented in this course are helpful and pretty easy to follow. My only complaint is - and this is true for a lot of these online courses - the programming assignments are way too easy.

I know this isn't a full-blown college level curriculum. I feel like I retain the material better when the assignments are more challenging.

por Roger S P M

29 de ene. de 2019

This is the second in the Advanced certificate series. By this time you are starting to understand their teaching method. So it is a better experience than the first one. Also you are getting more experience with the studio, cloudant, and Node-RED - which is very helpful and rewarding.

por Sonja T

8 de jul. de 2021

Good material. Hard to understand the instructors' English. Not professionally presented. Assignments are too easy, and we didn't get good, meaningful practice. Quizzes often address information that either the instructor failed to present well, if at all, or made mistakes on.

por Mohsen F

24 de dic. de 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.