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Digital Signal Processing 2: Filtering

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Digital Signal Processing 2: Filtering

This course is part of Digital Signal Processing Specialization

Instructors: Paolo Prandoni

20,646 already enrolled

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

198 reviews

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

198 reviews

Intermediate level

Some related experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Digital filters, how they work
Digital filter design
Adaptive signal processing

Skills you'll gain

Tools you'll learn

Jupyter

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

91% of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Digital Signal Processing Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices.

The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.