MathWorks
MathWorks Computer Vision Engineer Professional Certificate
MathWorks

MathWorks Computer Vision Engineer Professional Certificate

Advance your career with computer vision skills. Learn and apply the computer vision skills needed to effectively address real-world challenges experienced across many industries.

Sam Jones
Amanda Wang
Isaac Bruss

Instructors: Sam Jones

Sponsored by ITC-Infotech

1,817 already enrolled

Earn a career credential that demonstrates your expertise
4.7

(34 reviews)

Beginner level
No prior experience required
2 months
at 10 hours a week
Flexible schedule
Learn at your own pace
Earn a career credential that demonstrates your expertise
4.7

(34 reviews)

Beginner level
No prior experience required
2 months
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Automatically extract information from images

  • Detect and track objects in images and videos

  • Apply the full deep-learning workflow to computer vision challenges

  • Export models to common formats like Tensorflow or PyTorch

Details to know

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Taught in English

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Advance your career with in-demand skills

  • Receive professional-level training from MathWorks
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from MathWorks
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Professional Certificate - 9 course series

Introduction to Image Processing

Course 19 hours4.8 (151 ratings)

What you'll learn

  • Perform analysis on a variety of common image datatypes & recognize their strengths and limitations

  • Detect objects and regions of interest using intensity-based & color-based image segmentation

  • Improve image contrast using a variety of modern algorithms for different use-cases, such as low light or fog

  • Complete a project where you analyze Antarctic ice melt in satellite images

Image Segmentation, Filtering, and Region Analysis

Course 29 hours4.8 (41 ratings)

What you'll learn

  • Use segmentation to detect and analyze regions of interest in images & video

  • Apply spatial filters and morphological operators to improve segmentation & remove noise

  • Segment & analyze 3D images, such as MRI images of a brain

  • Use interactive tools to quickly test a variety of segmentation approaches & automatically generate code for reuse

Automating Image Processing

Course 313 hours4.7 (17 ratings)

What you'll learn

  • Apply image processing algorithms to large sets of images & verify your algorithms generalize to new images

  • Apply image processing algorithms to video files

  • Analyze your image & video processing results, including calculating statistics like average area & identifying outliers

  • Complete a specialization-level project where you will detect cars in a noisy video

Introduction to Computer Vision

Course 48 hours4.5 (47 ratings)

What you'll learn

  • Use common algorithms for feature detection, extraction, & matching

  • Perform image registration by identifying control points & estimating geometric transformations

  • Complete a final project where you stitch together images from NASA’s Mars Curiosity Rover

  • Combine images with image stitching to create panorama images

Machine Learning for Computer Vision

Course 511 hours4.8 (20 ratings)

What you'll learn

  • Prepare data and create features for classifying images

  • Train & evaluate models to classify images using

  • Train & evaluate object detection machine learning models

  • Customize model training for different applications using cost matrices

Object Tracking and Motion Detection with Computer Vision

Course 613 hours4.7 (27 ratings)

What you'll learn

  • Use pre-trained deep neural networks like YOLO to perform object detection

  • Use optical flow to detect motion & moving objects

  • Perform multi-object tracking to count, track, & determine the direction of objects

What you'll learn

  • Develop a strong foundation in deep learning for image analysis

  • Retrain common models like GoogLeNet and ResNet for specific applications

  • Investigate model behavior to identify errors, determine potential fixes, and improve model performance

  • Complete a real-world project to practice the entire deep learning workflow

What you'll learn

  • Retrain popular YOLO deep learning models for your applications

  • Visualize results to gain insights into model performance

  • Evaluate detection models by examining both class and location accuracy.

  • Analyze labeled images to identify and fix potential data shortcomings

What you'll learn

  • Train and calibrate specialized models known as anomaly detectors

  • Generate synthetic training images for situations where acquiring more data is expensive or impossible

  • Use AI-assisted auto-labeling to save time and money

  • Import models from 3rd party tools like PyTorch and export your model outside of MATLAB

Instructors

Sam Jones
MathWorks
3 Courses33,356 learners
Amanda Wang
MathWorks
9 Courses27,984 learners
Isaac Bruss
MathWorks
9 Courses49,976 learners

Offered by

MathWorks

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