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Advanced Machine Learning and Signal Processing(으)로 돌아가기

IBM의 Advanced Machine Learning and Signal Processing 학습자 리뷰 및 피드백

4.5
별점
1,059개의 평가
186개의 리뷰

강좌 소개

>>> 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 ibm.biz/badging....

최상위 리뷰

MM
2020년 4월 28일

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.

A
2018년 9월 7일

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.

필터링 기준:

Advanced Machine Learning and Signal Processing의 184개 리뷰 중 151~175

교육 기관: Filip G

2019년 9월 27일

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

교육 기관: Stefan T

2019년 12월 31일

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.

교육 기관: Jeramie G

2019년 9월 4일

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.

교육 기관: Roger S P M

2019년 1월 29일

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.

교육 기관: Björn ' H

2019년 9월 19일

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.

교육 기관: Michael P C

2020년 11월 5일

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.

교육 기관: Greg R

2020년 5월 12일

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.

교육 기관: Mario E R T

2019년 8월 8일

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.

교육 기관: BORVORNTAT N

2020년 7월 7일

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

교육 기관: Anastasiia S

2019년 9월 15일

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

교육 기관: Salvatore S

2020년 1월 11일

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

교육 기관: Thiago d S B

2020년 7월 7일

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

교육 기관: Ayushman S

2020년 7월 18일

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

교육 기관: Riku S

2019년 2월 7일

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

교육 기관: Nima

2020년 6월 8일

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

교육 기관: Mark B

2020년 4월 17일

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

교육 기관: Prashant B

2019년 8월 29일

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

교육 기관: Nicolas M

2020년 4월 17일

It should be useful to introduce more practical exercises

교육 기관: Markus W

2019년 9월 23일

well explained, programming assignments are worthless.

교육 기관: Santiago M L

2020년 6월 28일

It's a little bit comlicated develop the activitites

교육 기관: Onteddu R K R

2020년 5월 3일

assignments should be more challenging

교육 기관: Mattia S

2020년 5월 22일

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.

교육 기관: Xavier U

2020년 5월 3일

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.

교육 기관: Dmitry S

2020년 3월 10일

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

교육 기관: Jukka A

2019년 2월 16일

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.