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.

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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.

Course 2 of 4 in the Advanced Data Science with IBM Specialization.

Syllabus

WEEK 1: Setting the stage

WEEK 2: Supervised Machine Learning

WEEK 3: Unsupervised Machine Learning

WEEK 4: Digital Signal Processing in Machine Learning

Advanced Data Science with IBM Specialization

Signal Processing

Machine Learning

Digital Signal Processing

SparkML

Linear Algebra

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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing

Coursera

IBM

Romeo Kienzler