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Data Analysis Signal Processing

Course Reviews

Track :

Technology

Lessons no : 36

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What will you learn in this course?
  • Master techniques for extracting meaningful signals from noisy data using advanced signal processing methods
  • Apply digital filtering and Fourier analysis to enhance data quality and identify key patterns in real-world signals
  • Implement time-frequency analysis tools like wavelets to analyze non-stationary signals in practical applications
  • Utilize statistical and computational methods to detect, classify, and interpret signals in complex datasets
  • Design and optimize algorithms for real-time signal processing in engineering and communication systems
  • Evaluate signal integrity and troubleshoot issues using spectral analysis and noise reduction techniques
  • Develop skills to process and analyze signals in diverse fields such as telecommunications, biomedical engineering, and audio processing
  • Leverage machine learning approaches to improve signal detection, classification, and predictive modeling
  • Integrate signal processing techniques into data analysis workflows for improved accuracy and efficiency
  • Apply MATLAB, Python, or similar tools for practical signal analysis and visualization tasks
  • Understand the principles of digital signal processing to innovate in signal analysis and system design
  • Assess the impact of filtering, sampling, and quantization on signal quality and data interpretation

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  • The Certificate is free !
Lessons | 36
Course Introduction of 18 065 by Professor Strang
00:07:04

Course Introduction of 18 065 by Professor Strang
An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis Signal Processing
00:08:07

An Interview with Gilbert Strang on Teaching Matrix Methods in Data Analysis Signal Processing
Lecture 1 The Column Space of A Contains All Vectors Ax
00:52:15

Lecture 1 The Column Space of A Contains All Vectors Ax
Lecture 2 Multiplying and Factoring Matrices
00:48:26

Lecture 2 Multiplying and Factoring Matrices
3 Orthonormal Columns in Q Give Q Q I
00:49:24

3 Orthonormal Columns in Q Give Q Q I
4 Eigenvalues and Eigenvectors
00:48:56

4 Eigenvalues and Eigenvectors
5 Positive Definite and Semidefinite Matrices
00:45:28

5 Positive Definite and Semidefinite Matrices
6 Singular Value Decomposition SVD
00:53:34

6 Singular Value Decomposition SVD
7 Eckart Young The Closest Rank k Matrix to A
00:47:16

7 Eckart Young The Closest Rank k Matrix to A
Lecture 8 Norms of Vectors and Matrices
00:49:21

Lecture 8 Norms of Vectors and Matrices
9 Four Ways to Solve Least Squares Problems
00:49:51

9 Four Ways to Solve Least Squares Problems
Lecture 10 Survey of Difficulties with Ax b
00:49:36

Lecture 10 Survey of Difficulties with Ax b
Lecture 11 Minimizing ‖x‖ Subject to Ax b
00:50:22

Lecture 11 Minimizing ‖x‖ Subject to Ax b
12 Computing Eigenvalues and Singular Values
00:49:28

12 Computing Eigenvalues and Singular Values
Lecture 13 Randomized Matrix Multiplication
00:52:24

Lecture 13 Randomized Matrix Multiplication
14 Low Rank Changes in A and Its Inverse
00:50:34

14 Low Rank Changes in A and Its Inverse
15 Matrices A t Depending on t Derivative dA dt
00:50:52

15 Matrices A t Depending on t Derivative dA dt
16 Derivatives of Inverse and Singular Values
00:43:08

16 Derivatives of Inverse and Singular Values
Lecture 17 Rapidly Decreasing Singular Values
00:50:34

Lecture 17 Rapidly Decreasing Singular Values
Lecture 18 Counting Parameters in SVD LU QR Saddle Points
00:49:00

Lecture 18 Counting Parameters in SVD LU QR Saddle Points
19 Saddle Points Continued Maxmin Principle
00:52:13

19 Saddle Points Continued Maxmin Principle
20 Definitions and Inequalities
00:55:01

20 Definitions and Inequalities
Lecture 21 Minimizing a Function Step by Step
00:53:45

Lecture 21 Minimizing a Function Step by Step
22 Gradient Descent Downhill to a Minimum
00:52:44

22 Gradient Descent Downhill to a Minimum
23 Accelerating Gradient Descent Use Momentum
00:49:02

23 Accelerating Gradient Descent Use Momentum
24 Linear Programming and Two Person Games
00:53:34

24 Linear Programming and Two Person Games
25 Stochastic Gradient Descent
00:53:03

25 Stochastic Gradient Descent
26 Structure of Neural Nets for Deep Learning
00:53:17

26 Structure of Neural Nets for Deep Learning
27 Backpropagation Find Partial Derivatives
00:52:38

27 Backpropagation Find Partial Derivatives
Lecture 30 Completing a Rank One Matrix Circulants
00:49:53

Lecture 30 Completing a Rank One Matrix Circulants
31 Eigenvectors of Circulant Matrices Fourier Matrix
00:52:37

31 Eigenvectors of Circulant Matrices Fourier Matrix
Lecture 32 ImageNet is a Convolutional Neural Network CNN The Convolution Rule
00:47:19

Lecture 32 ImageNet is a Convolutional Neural Network CNN The Convolution Rule
33 Neural Nets and the Learning Function
00:56:07

33 Neural Nets and the Learning Function
34 Distance Matrices Procrustes Problem
00:29:17

34 Distance Matrices Procrustes Problem
35 Finding Clusters in Graphs
00:34:49

35 Finding Clusters in Graphs
Lecture 36 Alan Edelman and Julia Language
00:38:11

Lecture 36 Alan Edelman and Julia Language


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4.8
4 Reviews
Sonia Farah

excellent 2025-08-21

Sagar Rana

the course is good 2025-05-28

Rohit Kumar Shah

they all teaches good and thanks to providing us a valuable information about data analysis 2025-05-23

Pias Mia

Awesome 2024-02-26

rachel Zeitoun

Good 2024-02-25

firthoze Kuttyba

So good 2023-11-19

mohyeddin baghlani

very good 2023-11-17

Belay Sitotaw

Waw it was a great moment to attend the whole course. I thank the Prof. All training like what the Prof did, it will be very fascinating. 2023-11-09

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Signal is the real pattern, the repeatable process that we hope to capture and describe. It is the information that we care about. The signal is what lets the model generalize to new situations .