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Digital Signal Processing 2: Filtering
by Paolo Prandoni , Martin Vetterli- 4.7
Approx. 18 hours to complete
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. a Linear time-invariant filters...
Digital Signal Processing 4: Applications
by Paolo Prandoni , Martin Vetterli- 0.0
Approx. 14 hours to complete
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....
Digital Signal Processing 3: Analog vs Digital
by Paolo Prandoni , Martin Vetterli- 4.8
Approx. 16 hours to complete
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....
Differential Equations: Linear Algebra and NxN Systems of Differential Equations
by David Jerison , Bjorn Poonen , Jennifer French , Duncan Levear- 0.0
12 Weeks
Learn how to use linear algebra and MATLAB to solve large systems of differential equations. In this course, we will learn how to use linear algebra to solve systems of more than 2 differential equations....
$75
Digital Signal Processing 1: Basic Concepts and Algorithms
by Paolo Prandoni , Martin Vetterli- 4.6
Approx. 29 hours to complete
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....
Visual Perception for Self-Driving Cars
by Steven Waslander- 4.7
Approx. 31 hours to complete
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. 0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses). Welcome to Course 3: Visual Perception for Self-Driving Cars Welcome to the course Course Prerequisites How to Use Supplementary Readings in This Course...
Unsupervised Learning
by Mark J Grover , Miguel Maldonado- 4.9
Approx. 9 hours to complete
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. The hands-on section of this course focuses on using best practices for unsupervised learning. This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting. Course Introduction...
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Specialized Models: Time Series and Survival Analysis
by Mark J Grover , Miguel Maldonado- 4.5
Approx. 11 hours to complete
This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning. This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis. Course Introduction...
Unsupervised Machine Learning
by Mark J Grover , Miguel Maldonado- 4.8
Approx. 9 hours to complete
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. The hands-on section of this course focuses on using best practices for unsupervised learning. This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting. Course Introduction...
Guided Tour of Machine Learning in Finance
by Igor Halperin- 3.8
Approx. 24 hours to complete
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance....