Advanced Machine Learning and Signal Processing
Advanced Data Science with IBM,
>>> 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.
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Rating | 4.1★ based on 89 ratings |
---|---|
Length | 5 weeks |
Starts | Jul 3 (39 weeks ago) |
Cost | $79 |
From | IBM, IBM Skills Network via Coursera |
Instructors | Romeo Kienzler, Nikolay Manchev |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Algorithms Machine Learning |
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What people are saying
signal processing
I would like to recommend this course this is really interesting and most interesting part is the signal processing which builds an proper understanding of the math buzzwords like fourier and wavelet transform.5 stars to the course Programming assignments were not challenging.
This course covers many traditional approaches to machine learning and signal processing.
The Signal Processing was eye-opening as a way to extract data from sampled data.
Personally, however, I didn't think the digital signal processing portion was as useful as the first three weeks.
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machine learning
best course to gross, even for beginer 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.
There was nothing "advanced" about the machine learning, and image processing was only gone over in the final week, and it was mostly just an overview of the important topics/concepts.
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programming assignments
I only wish the programming assignments were a little more interactive and deeper than "fill in the blank."
On top I can't figure out why did the authors put in a whole week on Fourier Transformation.. :S well explained, programming assignments are worthless.
My only complaint is - and this is true for a lot of these online courses - the programming assignments are way too easy.
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too easy
Assignments were a bit too easy.
The assignments are way too easy.
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very good
Very good, only the english from guest lecture is horrible.
The lectures and assignments are very good.
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Rating | 4.1★ based on 89 ratings |
---|---|
Length | 5 weeks |
Starts | Jul 3 (39 weeks ago) |
Cost | $79 |
From | IBM, IBM Skills Network via Coursera |
Instructors | Romeo Kienzler, Nikolay Manchev |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Algorithms Machine Learning |
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