Master MATLAB through Guided Problem Solving

Become an expert in MATLAB Programming and Scientific Computing. Advance your career in Engineering Physics Biology etc

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Michael Cohen
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MATLAB is the premier programming language of scientific computing in university, research, and industry.

Excellent MATLAB programming skills can make or break a career in engineering, physics, biology, finance, and many other fields.

But how do you learn how to program in MATLAB? Sure, you can watch some tutorial videos online, but those videos generally focus on the most basic coding skills, like what is a variable and how to write a for-loop.

Those are important foundations, but they won’t make you competitive.

If you want to be competitive, you need to become a master at MATLAB programming. And that’s what you’ll learn in this course.

This course is different from other MATLAB programming courses!

Rather than having short videos that presents a simple piece of MATLAB syntax, this course starts from the real-world perspective: Problems in data science that you can solve with MATLAB.

Do you need to learn how to filter your signal in MATLAB?  There’s a section for that!

Do you have a complicated formula and you need to translate it into MATLAB code and make a graph that simulates the formula with different parameters? Yeah, there’s a section on that as well!

Do you need to make a movie of a 3D simulation changing in color over time and then export as an .avi file? This course covers that as well!   

Maybe you want to learn how to make textured landscapes using fractal math, or use SVD to compute the low-rank approximation of a data matrix, or use nonlinear search methods to fit a 3-parameter Gaussian model to data? You guessed it — it’s all covered in this course!

If you are looking for a slow, gentle course that will take baby-steps to teach you what you could have learned on your own, then this is not that course.

If you want to gain confidence in solving real-world problems in MATLAB coding to advance your studies, your research, and your career by learning MATLAB programming from a single course with >35 hours of hands-on instruction from a world-class scientist, teacher, and writer, then this course is definitely right for you.

Key topics covered.

The basic stuff: loops, functions, IO, etc. Core skills you need to be able to progress with MATLAB.

The more intermediate and advanced topics in the course include:

2D and 3D plotting, and data animations (movies)

Image and time series segmentation

Calculus and differential equations

Univariate and multivariate time series cleaning and analysis

Spectral (Fourier) and time-frequency analyses

Linear algebra including eigendecomposition, orthogonalization, and principal components analysis

Generating fractal pictures and time series

Nonparametric statistics and nonlinear model fitting

Get started today!

There is no doubt that MATLAB is the premier programming language of scientific computing in university, research, and industry.

The sooner you sign up for this course, the sooner you will be able to advance your career in engineering, physics, biology, finance (or other fields using MATLAB).

Why not get started today?

Click the Signup button to sign up for the course!

Requirements

  • A computer with MATLAB or Octave installed.
  • Many people have access to MATLAB through their school, university, or company.
  • MATLAB also offers student licenses and free 30-day trials.
  • Octave is a free cross-platform scientific computing environment that has nearly identical functionality as MATLAB.
  • Some previous experience with MATLAB programming is desirable.
  • This course starts from the basics but builds quickly to intermediate/advanced programming levels.
  • Previous experience with other programming languages will be beneficial but is not necessary.

What you will learn

  • Beginning to advanced MATLAB programming proficiency. This is *the only course* that develops intermediate and advanced programming skills.
  • Obtain real-world application experience that that researchers and industry professionals use MATLAB for, including signal processing, matrix decompositions, spectral analysis, linear and nonlinear model-fitting, and statistics.
  • Gain skills to solve challenging problems in MATLAB, as opposed to memorizing syntax rules.

Who should attend

  • Anyone looking to build a career in engineering, physics, biology, finance, and many other fields by building Excellent MATLAB coding skills
  • Anyone wanting to progress beyond watching simple MATLAB tutorials to actually becoming an expert in MATLAB programming by solving real problems in scientific computing

Is available on google app?

Yes

Is available on ios?

Yes

Course Features

  • Lectures 171
  • Quizzes 0
  • Exercises 0
  • Duration 38 hours
  • Skill level All Levels
  • Students 7578
  • Last Updated July, 2019

  • Course Introduction 16:33

    • Lecture
      (Important) How to Learn from this Course 11:20
    • Lecture
      Stages of Learning Programming and Completing Projects 05:11
    • Lecture
      pdf with complete lecture list and keywords 00:02

  • Getting Started 54:41

    • Lecture
      Create, edit, and open scripts 16:33
    • Lecture
      Write Comments in Lines and Blocks 09:52
    • Lecture
      Using MATLAB for a Personal Budget 16:22
    • Lecture
      Personalize MATLAB Colors 06:40
    • Lecture
      Start MATLAB with an Encouraging Note 05:14

  • Vectors and Variables 54:19

    • Lecture
      Create Vectors and Matrices 11:11
    • Lecture
      Unsolved: Valid and Invalid Matrices 00:50
    • Lecture
      Working with Text (Characters and Strings) 14:23
    • Lecture
      HTML Table from MATLAB Code 10:08
    • Lecture
      Round pi to N significant Digits 07:17
    • Lecture
      File/folder Information Using Structures 10:30

  • Command Statements 50:30

    • Lecture
      Create a Hilbert Matrix Using for-loops. 06:58
    • Lecture
      Unsolved: Compare two while Loops 01:02
    • Lecture
      Create an Upper-Triangular Matrix 09:57
    • Lecture
      Random Count-Down Timer (Poisson-like) 19:51
    • Lecture
      Display the day of 1 January 12:42

  • Import and Export Data 01:12:35

    • Lecture
      Save and Load Multiple Files 16:48
    • Lecture
      Import Formatted Text Data 19:15
    • Lecture
      Import Excel-format Data 26:45
    • Lecture
      Convert US$ to Euros Using up-to-date Info 09:47

  • Translate Formulas into Code 01:44:56

    • Lecture
      Trig Functions and Gaussians 19:07
    • Lecture
      Laplace and log-normal Distributions 17:41
    • Lecture
      Complex Numbers and Euler's Formula 14:55
    • Lecture
      Piecewise Functions 18:35
    • Lecture
      Piecewise Function in one line of Code 10:06
    • Lecture
      Sigmoid Function 09:32
    • Lecture
      Unsolved: Sigmoid and Error Function 01:07
    • Lecture
      Circular p-value and its Approximation 13:53

  • Descriptive Statistics 41:35

    • Lecture
      Compute Measures of Central Tendency 17:48
    • Lecture
      Compute Variance and Standard Deviation 10:16
    • Lecture
      Unsolved: Sort Data up and down 00:54
    • Lecture
      Data Transformations (log, sqrt, rank) 12:37

  • 2D Plotting 03:18:31

    • Lecture
      Lines 15:22
    • Lecture
      Bars and errorbars 18:23
    • Lecture
      Dots 14:57
    • Lecture
      Multidimensional Data with Colored Scatter 10:44
    • Lecture
      Unsolved: imagesc vs. pcolor 01:03
    • Lecture
      Histograms 19:44
    • Lecture
      Uncertainty in Future Money (using patch) 23:17
    • Lecture
      Blend Pictures Using Transparency 28:58
    • Lecture
      Vertically Stacking Data Series 14:58
    • Lecture
      Distance Matrix from Generated Points 25:44
    • Lecture
      Gabor Patch Marginal Histograms 25:02
    • Lecture
      List of other image-processing videos in this course 00:19

  • 3D Plotting 02:19:34

    • Lecture
      Sphere in a Cube 19:50
    • Lecture
      Colorful Cube (a.k.a. the happy Borg ship) 21:07
    • Lecture
      Expanding Wavelets with Surfaces 15:08
    • Lecture
      Textured Gaussian Surfaces 16:01
    • Lecture
      A Ball in 3D Color Space 29:03
    • Lecture
      Plane in R3 Spanned by Two Vectors 10:46
    • Lecture
      Complex Sinc Surface 12:47
    • Lecture
      The Prickly Gabor Patch 14:52

  • Segmentation 01:32:34

    • Lecture
      Threshold-based Time Series Segmentation 26:04
    • Lecture
      Derivative-based Time Series Segmentation 18:19
    • Lecture
      Intensity-based Image Segmentation 21:32
    • Lecture
      Identify Neurons in a Mouse Brain Slice 26:39

  • Data Animations 01:39:02

    • Lecture
      Random Floating Ball 19:47
    • Lecture
      The Square Chases the Mouse 11:20
    • Lecture
      The Magically Materializing Peaks 22:44
    • Lecture
      Smooth Sailing: The Movie 13:27
    • Lecture
      Real-time Audio Spectrum from Mic 14:31
    • Lecture
      Möbius Transformation 16:03
    • Lecture
      UFO on a Sandcastle 01:10

  • Graphical User Interfaces 01:14:18

    • Lecture
      Dialog Box for User Input 13:38
    • Lecture
      Interface to Select a File 04:56
    • Lecture
      Input and Message Boxes 08:04
    • Lecture
      GUI to Create Random Landscapes 17:10
    • Lecture
      GUIDE to Sigmoid Parameter Space 30:30

  • Functions and Anonymous Functions 01:10:16

    • Lecture
      Same-length Differentiation 24:56
    • Lecture
      Damped Oscillator 11:57
    • Lecture
      Unsolved: Damped Arcsine 01:45
    • Lecture
      Find and Extract a Function Core 09:01
    • Lecture
      Smooth Plotting Function with Options 21:16
    • Lecture
      Unsolved: Zscore Function 01:21

  • Find, Min, Max 01:18:07

    • Lecture
      Find Point Closest to Specified Value 13:43
    • Lecture
      Unsolved: Manual Peak-Picking 00:53
    • Lecture
      Find Negative Extrema in a 2D Function 16:31
    • Lecture
      Unsolved: Find Ridges of a 2D Surface 01:10
    • Lecture
      Find Local Maxima 15:09
    • Lecture
      Replace Image Pixels in an Intensity Range 17:37
    • Lecture
      Find Signal Clipping Points 13:04

  • Calculus and Differential Equations 57

    • Lecture
      Plotting Symbolic Functions 11:34
    • Lecture
      Function Limits 09:34
    • Lecture
      Function Derivatives 11:06
    • Lecture
      Function Integration 14:19
    • Lecture
      Solving Differential Equations 10:27

  • Cleaning Univariate Time Series 01:31

    • Lecture
      Running Mean Flter 15:14
    • Lecture
      Threshold Median Filter 19:05
    • Lecture
      Unsolved: all-points Median Filter 01:15
    • Lecture
      Interpolate Missing Time Points 20:13
    • Lecture
      Spectral Mixing Interpolation 15:35
    • Lecture
      Polynomial Fitting to Remove Drifts 17:14
    • Lecture
      Unsolved: Polynomial Fitting to Isolate Drifts 01:14
    • Lecture
      Unsolved: Local Maximima in Noisy Data 01:10

  • Cleaning Multivariate Time Series 02:18:33

    • Lecture
      Three Ways to make a Covariance Matrix 18:36
    • Lecture
      Reject Data Based on Extreme Covariance 16:45
    • Lecture
      Effects of Averaging on Covariance Matrices 29:44
    • Lecture
      Simulate Tri-Component Time-Space Data 17:37
    • Lecture
      Unsolved: Tri-component Data without Loops 01:32
    • Lecture
      Space-Based Single Channel Interpolation 23:49
    • Lecture
      Spatial Smoothing on a Grid of Channels. 17:12
    • Lecture
      Spatial Sharpening via Laplacian 13:18

  • Time Series Analysis 03:05:34

    • Lecture
      Convolution 24:17
    • Lecture
      High-pass Filter using FIR Filter 17:33
    • Lecture
      Narrow-band Filter via Frequency-Domain Gaussian 22:04
    • Lecture
      Causal vs. Zero-Phase-Shift Filter 16:14
    • Lecture
      Line Noise Notch Filter 20:41
    • Lecture
      Compute Envelope Over Peaky, Noisy Signal 20:19
    • Lecture
      Frequency-domain Mean Filter 17:31
    • Lecture
      Create a "chirp" (FM Signal) 19:56
    • Lecture
      Detrended Fluctuation Analysis 26:59

  • Spectral Analysis 01:52:17

    • Lecture
      Create Multispectral Time Series 11:49
    • Lecture
      Power Spectrum from FFT and Welch's Method 23:24
    • Lecture
      Spectrogram of Bird Call 18:56
    • Lecture
      Phase-scramble Narrowband Time Series 12:59
    • Lecture
      Hilbert Transform 11:28
    • Lecture
      Oscillations in Human Brain Recordings 08:16
    • Lecture
      Time Frequency Analysis via Wavelet Convolution 23:39
    • Lecture
      Unsolved: Time-frequency Analysis of Interpolated Signal 01:46

  • Matrix Analysis 03:12:16

    • Lecture
      Four Ways to Compute the Dot Product 06:59
    • Lecture
      Unsolved: Plot Vectors and Compute Lengths 01:32
    • Lecture
      Hermitian vs. Regular Transpose 09:41
    • Lecture
      Create a Symmetric Matrix: Additive 05:48
    • Lecture
      Create a Square Symmetric Matrix: Multiplicative 07:26
    • Lecture
      MxM Matrix with Rank M-1 11:04
    • Lecture
      MxN Matrix with Rank r via SVD 13:41
    • Lecture
      Create a Random Hankel Matrix 08:44
    • Lecture
      Unsolved: Element-wise Hankel Matrix with Mod Function 01:09
    • Lecture
      Unsolved: Create a Toeplitz Matrix 01:25
    • Lecture
      Eigenvectors of a Hankel Matrix 18:18
    • Lecture
      Compute a Unit Vector in some Direction 04:24
    • Lecture
      Orthogonalize a Pair of Vectors 09:31
    • Lecture
      Gram-Schmidt Algorithm 14:51
    • Lecture
      Matrix Inverse via QR Decomposition 10:58
    • Lecture
      Visualize the Quadratic form of a 2x2 Matrix 11:29
    • Lecture
      Eigenvectors and Quadratic Form 11:36
    • Lecture
      PCA of Low-Rank Space-Time Data 23:30
    • Lecture
      Covariance Shrinkage Regularization in PCA 20:10

  • Circular Distributions and Analyses 56:24

    • Lecture
      Draw a Vector in Polar Plane 12:57
    • Lecture
      Circular Histogram 10:39
    • Lecture
      Compute and Plot Mean Vector Length 11:42
    • Lecture
      Phase Difference Between Two Distributions 21:06

  • Fractal Time Series and Images 01:21:07

    • Lecture
      The Sierpinksi Triangle 14:45
    • Lecture
      Unsolved: Sierpinksi Triangle as Dense Matrix 01:54
    • Lecture
      Brownian Motion 04:19
    • Lecture
      Cantor Set and Devil's Staircase 14:51
    • Lecture
      Unsolved: Initialize the Cantor Set 01:39
    • Lecture
      Mandelbrot Set 13:55
    • Lecture
      Weierstrass Function 17:57
    • Lecture
      Fractal Circles and Bubbles 11:47

  • Nonparametric Statistics 01:45:42

    • Lecture
      Wilcoxon Rank Test 10:11
    • Lecture
      2D Space of Wilcoxon Effect Sizes 12:15
    • Lecture
      KL Divergence of Two Distributions 15:28
    • Lecture
      2D Space of KL Divergences 13:06
    • Lecture
      Permutation Testing 27:22
    • Lecture
      Bootstrapping for Confidence Intervals 23:39
    • Lecture
      Unsolved: Bootstrapping Medians 03:41

  • Nonlinear Model Fitting 01:43:53

    • Lecture
      Find a Function Minimum 16:57
    • Lecture
      Fit a Gaussian to a Distribution 23:25
    • Lecture
      Two-piece Linear Regression 29:45
    • Lecture
      Fit a Sine Wave 10:26
    • Lecture
      Fit a Circle to a Noisy Ring 23:20

  • Bonus Material 15:17

    • Lecture
      Using the Q&A forum 04:47
    • Lecture
      Bonus - Please Watch! 00:48
    • Lecture
      Check out Our Other 41 Udemy Courses! 00:35
    • Lecture
      Source Codes 08:28
    • Lecture
      ALL MATLAB materials for the entire course in one zip file 00:00
    • Lecture
      Bonus: Related courses from Mike X Cohen 00:39
Michael Cohen
Neuroscientist, teacher, writer I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European… View Profile
Tim Buchalka's Learn Programming Academy
Professional Programmers and Teachers - 323,000+ students The Learn Programming Academy was created by Tim Buchalka, a software developer with 35 years experience, who is also an instructor on Udemy, with over 335,000 students in his courses… View Profile

Student Feedback

4.6
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  • 8 months ago

    Akif ARSLAN

    Teacher is very clear and informative

  • 9 months ago

    Steve W

    A very comprehensive course with a massive amount of material. As with Mike’s other courses there’s a logical flow in following the course as intended, but it’s also possible to cherry-pick the info you need as the topics are bite-sized and well indexed.

  • 9 months ago

    Kiran C Shekar

    i really wanted a course like this.

  • 9 months ago

    Victor Manuel Caranqui Sánchez

    Excelente

  • 9 months ago

    Laura Morris

    I was expecting some dry "how to" instructional videos but MXC goes above and beyond! Oh, how I wish my university professors were all like him. If you're thinking about taking a course from this instructor let me suggest the answer is "yes, Yes, YES!".

  • 9 months ago

    Tuna Serdar

    yes i am very gratefull to myself for attending this course it goes so clear and understandable right now

  • 9 months ago

    Arturo Ignacio Espinoza

    So far this has been a great experience. I am a data scientist in training with an emphasis on neuroscience, so I know this is the right course for me. I am hoping I will be able to apply what I learn when I do data analysis.

  • 10 months ago

    Gusti Triandi Winata

    This is what i've searching for. still just getting started, but after skimming the course, the contents is really great!

  • 10 months ago

    karshankumar Gangdas Chaudhari

    Way of teaching is best, before I comment about content I need to attend more lectures. Thank you.

  • 10 months ago

    Tarek Khazaal

    Untertitel sind sehr fehlerhaft

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