About this Course
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Advanced Level

Approx. 16 hours to complete

Suggested: 15 hours/week...


Subtitles: English
Learners taking this Course are
  • Data Scientists
  • Machine Learning Engineers
  • Traders
  • Data Analysts
  • Researchers

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 16 hours to complete

Suggested: 15 hours/week...


Subtitles: English

Syllabus - What you will learn from this course

4 hours to complete

Setting the stage

10 videos (Total 59 min), 1 reading, 3 quizzes
10 videos
Linear algebra5m
High Dimensional Vector Spaces2m
Supervised vs. Unsupervised Machine Learning4m
How ML Pipelines work3m
Introduction to SparkML20m
What is SystemML (1/2) ?3m
What is SystemML (2/2) ?6m
How to use Apache SystemML in IBM Watson Studio4m
Extract - Transform - Load3m
1 reading
Object Store10m
2 practice exercises
Machine Learning12m
ML Pipelines6m
6 hours to complete

Supervised Machine Learning

26 videos (Total 131 min), 1 reading, 10 quizzes
26 videos
LinearRegression with Apache SparkML6m
Linear Regression using Apache SystemML3m
Batch Gradient Descent using Apache SystemML8m
The importance of validation data to prevent overfitting3m
Important evaluation measures2m
Logistic Regression1m
LogisticRegression with Apache SparkML4m
Probabilities refresher6m
Rules of probability and Bayes' theorem10m
The Gaussian distribution4m
Bayesian inference4m
Bayesian inference - example9m
Maximum a posteriori estimation5m
Bayesian inference in Python8m
Why is Naive Bayes "naive"7m
Support Vector Machines3m
Support Vector Machines using Apache SparkML8m
Hyper-parameter tuning using GridSearch3m
Decision Trees2m
Bootstrap Aggregation (Bagging) and RandomForest1m
Boosting and Gradient Boosted Trees6m
Gradient Boosted Trees with Apache SparkML2m
Hyperparameter-Tuning using GridSeach and CrossValidation in Apache SparkML on Gradient Boosted Trees3m
1 reading
Classification evaluation measures10m
9 practice exercises
Linear Regression6m
Splitting and Overfitting2m
Evaluation Measures2m
Logistic Regression2m
Naive Bayes16m
Support Vector Machines2m
Testing, X-Validation, GridSearch4m
Enselble Learning4m
5 hours to complete

Unsupervised Machine Learning

13 videos (Total 67 min), 1 reading, 3 quizzes
13 videos
Introduction to Clustering: k-Means3m
Hierarchical Clustering3m
Density-based clustering (Guest Lecture Saeed Aghabozorgi)4m
Using K-Means in Apache SparkML2m
Curse of Dimensionality9m
Dimensionality Reduction4m
Principal Component Analysis6m
Principal Component Analysis (demo)6m
Covariance matrix and direction of greatest variance8m
Eigenvectors and eigenvalues8m
Projecting the data4m
PCA in SystemML2m
1 reading
Reading on Clustering Evaluation and Assessment10m
2 practice exercises
5 hours to complete

Digital Signal Processing in Machine Learning

13 videos (Total 108 min), 3 quizzes
13 videos
Fourier Transform in action6m
Signal generation and phase shift11m
The maths behind Fourier Transform11m
Discrete Fourier Transform16m
Fourier Transform in SystemML15m
Fast Fourier Transform7m
Nonstationary signals5m
Continous Wavelet Transform3m
Scaling and translation3m
Wavelets and Machine Learning3m
Wavelets transform and SVM demo6m
2 practice exercises
Fourier Transform16m
Wavelet Transform16m
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Top reviews from Advanced Machine Learning and Signal Processing

By ASep 8th 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.

By JJJan 1st 2019

Such great material. I really loved working out the notebooks. I have to go back and redo the IoT starter exercise to get better accuracy, but this was awesome!



Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

Nikolay Manchev

Senior Data Scientist
IBM EMEA Data Science (2015-2019)

About IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

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. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging....
Advanced Data Science with IBM

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

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