This course is part of the Advanced Data Science with IBM Specialization

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Romeo Kienzler +1 more instructor

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Advanced Data Science with IBM SpecializationIBM Skills Network

About this Course

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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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Course 2 of 4 in the

Advanced Data Science with IBM Specialization

Advanced Level

Approx. 28 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 4 in the

Advanced Data Science with IBM Specialization

Advanced Level

Approx. 28 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.52/5 (172 Ratings)

Romeo Kienzler

Chief Data Scientist, Course Lead

IBM Watson IoT

496,566 Learners

9 Courses

Nikolay Manchev

Senior Data Scientist

IBM EMEA Data Science (2015-2019)

42,133 Learners

1 Course

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

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Syllabus - What you will learn from this course

Content Rating84%(3,711 ratings)

Week1

Week 1

7 hours to complete

Setting the stage

7 hours to complete

10 videos (Total 59 min), 3 readings, 4 quizzes

10 videos

A warm welcome3mLinear algebra5mHigh Dimensional Vector Spaces2mSupervised vs. Unsupervised Machine Learning4mHow ML Pipelines work3mIntroduction to SparkML20mWhat is SystemML (1/2) ?3mWhat is SystemML (2/2) ?6mHow to use Apache SystemML in IBM Watson Studio4mExtract - Transform - Load3m

3 readings

Object Store10mIMPORTANT: How to submit your programming assignments10mHands-on Lab: Sign Up for IBM Cloud Account20m

2 practice exercises

Machine Learning30mML Pipelines30m

Week2

Week 2

10 hours to complete

Supervised Machine Learning

10 hours to complete

26 videos (Total 131 min), 1 reading, 10 quizzes

26 videos

Linear Regression5mLinearRegression with Apache SparkML6mLinear Regression using Apache SystemML3mBatch Gradient Descent using Apache SystemML8mThe importance of validation data to prevent overfitting3mImportant evaluation measures2mLogistic Regression1mLogisticRegression with Apache SparkML4mProbabilities refresher6mRules of probability and Bayes' theorem10mThe Gaussian distribution4mBayesian inference4mBayesian inference - example9mMaximum a posteriori estimation5mBayesian inference in Python8mWhy is Naive Bayes "naive"7mSupport Vector Machines3mSupport Vector Machines using Apache SparkML8mCrossvalidation1mHyper-parameter tuning using GridSearch3mDecision Trees2mBootstrap Aggregation (Bagging) and RandomForest1mBoosting and Gradient Boosted Trees6mGradient Boosted Trees with Apache SparkML2mHyperparameter-Tuning using GridSeach and CrossValidation in Apache SparkML on Gradient Boosted Trees3mRegularization3m

1 reading

Classification evaluation measures10m

9 practice exercises

Linear Regression30mSplitting and Overfitting30mEvaluation Measures30mLogistic Regression30mNaive Bayes30mSupport Vector Machines30mTesting, X-Validation, GridSearch30mEnselble Learning30mRegularization30m

Week3

Week 3

5 hours to complete

Unsupervised Machine Learning

5 hours to complete

13 videos (Total 67 min), 1 reading, 3 quizzes

13 videos

Introduction to Unsupervised Machine Learning3mIntroduction to Clustering: k-Means3mHierarchical Clustering3mDensity-based clustering (Guest Lecture Saeed Aghabozorgi)4mUsing K-Means in Apache SparkML2mCurse of Dimensionality9mDimensionality Reduction4mPrincipal Component Analysis6mPrincipal Component Analysis (demo)6mCovariance matrix and direction of greatest variance8mEigenvectors and eigenvalues8mProjecting the data4mPCA in SystemML2m

1 reading

Reading on Clustering Evaluation and Assessment10m

2 practice exercises

Clustering30mPCA30m

Week4

Week 4

6 hours to complete

Digital Signal Processing in Machine Learning

6 hours to complete

13 videos (Total 108 min)

13 videos

Signal decomposition, time and frequency domains8mFourier Transform in action6mSignal generation and phase shift11mThe maths behind Fourier Transform11mDiscrete Fourier Transform16mFourier Transform in SystemML15mFast Fourier Transform7mNonstationary signals5mScaleograms7mContinous Wavelet Transform3mScaling and translation3mWavelets and Machine Learning3mWavelets transform and SVM demo6m

2 practice exercises

Fourier Transform30mWavelet Transform30m

Reviews

4.5

221 reviews

5 stars
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4 stars
22.76%
3 stars
7.20%
2 stars
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1 star
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TOP REVIEWS FROM ADVANCED MACHINE LEARNING AND SIGNAL PROCESSING

by ZZDec 31, 2018

fairly good. not perfectly organized but a little bit relax. good pace for workshop style training for some parts and enough details for some other parts.

by XNApr 14, 2019

It is kind of good, However, I wish there could be more exercise, I think online learning courses should provide intensive exercises instead just a scratch of the knowledge.

by PSNov 15, 2019

Great course. Finally after learning Transformation methods like Fourier and Wavelet, I finally got to learn real life problem solving capabilities of them. Learned a lot!!!!!

by MMApr 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.

View all reviews

About the Advanced Data Science with IBM Specialization

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Advanced Machine Learning and Signal Processing

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