Machine Vision using Python
Machine vision using Python: apply deep learning and computer vision with OpenCV and TensorFlow for real-world image classification and ecological data applications.
Duration: 40 Hours
Format: Recorded ‘on-demand’ Format
£450Registration Fee
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5.0
from 200+ reviews
- Overview
- Instructors
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Course Description
Machine vision has produced many helpful image-processing techniques in several fields, such as object detection, classification, and segmentation. Machine vision is an interdisciplinary discipline combining computer vision and machine learning methods, mainly deep learning, to solve vision problems. Common problems, such as classification and localisation, are typical examples that combine these research fields. These techniques have applications in many areas. Deep learning methods are commonly applied for image classification, focusing on deep neural networks and Convolutional Neural Networks (CNNs), including concepts of transfer learning applied to image classification. This course introduces basic concepts of deep learning and machine vision applied to image classification using CNNs. To illustrate these methods, a dataset of medically and forensically important flies is used. Other examples will also be used during the course to illustrate the applications of machine vision in ecology.
What You’ll Learn
During the course we will cover the following:
- The basic concepts behind the machine vision ecosystem in Python.
- The machine vision pipeline workflow.
- Understand the application of standard Python packages such as OpenCV and Tensorflow.
- Understand the basic concepts behind Deep Neural Networks.
- Understand the basic concepts behind Convolutional Deep Neural Networks;
- Understand basic concepts behind Transfer learning.
- Have the confidence to implement basic Machine vision methods using Python.
- Have the confidence to combine basic computer vision and machine learning methods to perform vision tasks.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is aimed at academics and postgraduate students working on projects related to machine vision, as well as applied researchers and analysts in public, private, or third-sector organisations who require the reproducibility, speed, and flexibility of a programming language such as Python for machine vision applications. It is also suitable for ecologists who use Python to address vision-related challenges and wish to update their expertise in this area. Participants should have a basic understanding of statistical and mathematical concepts, along with some familiarity with supervised learning. The first day of the course will cover the fundamentals of Python relevant to the module, though prior experience with any programming language will be beneficial.
Equipment and Software requirements
A laptop computer with a working version of Python is required. Python is free and open-source software for PCs, Macs, and Linux computers.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop.
Dr. Gabriel Palmer
Session 1 – 04:00:00 – Python Essentials for Machine Vision.
This section focuses on Python syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions.
Session 2 – 04:00:00 – Data Structures and File Handling in Python.
Focuses on lists, dictionaries, tuples, sets, and reading/writing files (e.g., CSVs) for data manipulation.
Session 3 – 04:00:00 – Introduction to Computer Vision and Image Processing.
This section covers the fundamental structure of an image, basic image handling techniques, and an introduction to computer graphics.
Session 4 – 04:00:00 – Local Image Descriptors and Feature Mapping.
This section explores local image descriptors, such as the Harris Corner Detector, and techniques for image-to-image mapping.
Session 5 – 04:00:00 – Section 5 (Neural Networks: From Basics to Backpropagation.
Introduces artificial neurons and explains how neural networks learn through backpropagation.
Session 6 – 04:00:00 – Section 6 (Convolutional Neural Networks (CNNs) for Image Classification.
Provides a detailed explanation of CNN architecture, including convolution layers, pooling layers, and fully connected layers.
Session 7 – 04:00:00 – Section 7 (Building Deep Learning Models with TensorFlow/Keras.
Offers a step-by-step guide to building CNN models for image classification using TensorFlow/Keras.
Session 8 – 04:00:00 – Section 8 (Image Processing with OpenCV: Filters, Edge Detection & Contours.
Covers basic image manipulation techniques using OpenCV, including resizing, cropping, applying filters (blurring/sharpening), edge detection (Canny), and contour detection.
Session 9 – 04:00:00 – Section 9 (Preprocessing Images for Deep Learning with OpenCV & TensorFlow.
This section focuses on preprocessing techniques in OpenCV before feeding images into TensorFlow models for training. An entomological example illustrating the Machine Vision Pipeline will be used.
Session 10 – 04:00:00 – Section 10 (The Complete Machine Vision Pipeline: From Image Capture to Classification.
Covers the end-to-end machine vision workflow, including image capture, enhancement through preprocessing, segmentation, feature extraction, and classification using machine learning classifiers.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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