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Machine Learning Engineering

AI Course: Computer Vision

US Online

Unlock the world of Computer Vision with our comprehensive course that takes you from the fundamentals of Deep Learning to advanced applications in image recognition, segmentation, and generative models. Enroll now to embark on a journey that will empower you with the knowledge and skills to excel in the dynamic field of Computer Vision. Take the first step toward becoming an expert in image analysis, recognition, and generation. Let’s explore the limitless possibilities of Computer Vision together!

 

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Part-time
Online Live
8 weeks
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About the Course

This course begins with an introduction to Deep Learning, basic computer vision, and then moves on to discuss CNN architectures and various use cases of of computer vision. Moreover, this course explores generative models for Computer Vision use cases in more details.

WHAT YOU WILL LEARN

  • Use OpenCV to prepare image data and perform basic image recognition and segmentation tasks
  • Apply different CNN architectures to solve image classification problems
  • Use pre-trained CNN models for transfer learning
  • Work on common computer vision tasks such as object detection, semantic segmentation, instance segmentation
  • Use GAN to generate images and neural style transfer
  • Fine-tune Stable Diffusion model for text-to-image generation tasks

Case-based learning with real-life examples

  • Medical image analysis.
  • Weather forecast using satellite images and time series
  • Image classification with ImageNet and CN
  • Handwritten Character Recognition

WeCloudData is the perfect place to grow your career

5/5

Alex V, Alumni Review
I can already tell Indrani is an incredible teacher.  She’s very knowledgeable. Her teaching style makes the material more memorable.  She asks questions which forces me to reread over my previous notes. Instructor Yi instills a lot of confidence in his knowledgeability. You can tell from his cadence and confidence that he knows what he’s teaching very well.

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CURRICULUM

Be ready for the new economy

WeCloudData programs are designed to be project-based. We not only cover essential theories, but also teach how to apply tools and platforms that are in high demand today. Our program curriculum is also highly adaptive to the latest market trends. 

MODULE 1
Machine Learning Review
The Machine Learning Review Module focuses on picking up basic ML Skills before diving deeply in the Deep Learning and Computer Vision. The module covers the classical ML Algorithms supervised and unsupervised learning, data and feature preparation techniques.
LEARNING OUTCOMES
  • Being able to develop Classical ML algorithms to solve several business cases related to predication and classification.
  • Evaluating and Assessing the performance of Machine Learning Models, utilizing different methods like confusion matrix, Precision, Recall, Specificity, accuracy, F1 Score, Mean Squared Error, R2 and Homogeneity.
  • Spotting different dataset issues like balanced training data.
  • Comparison of performance measures.

KEY SKILLS
  • Developing Machine Learning models
  • Evaluating Machine Learning models performance
Tools:
Python
Pandas
Sklearn
Tools:
Pytorch
Jupyter Notebooks
MODULE 2
Deep Learning Intro
This module discovers the Deep Learning History, the limitations of machine learning systems. How to build powerful deep learning models. Introducing PyTorch package for deep learning projects. Neural Nets fundamentals covering; the neural net architecture, activation functions, optimization methods, regularization techniques and batch normalization.
LEARNING OUTCOMES
  • Explore neural network architecture, activation functions, optimization methods, regularization techniques, and batch normalization.
  • Choose and implement appropriate activation functions for different scenarios.
  • Explore optimization algorithms used to train neural networks effectively.
  • Understand techniques for adjusting model parameters to minimize loss.

KEY SKILLS
  • Gain proficiency in using the PyTorch framework for implementing deep learning projects
  • Develop the ability to design and configure neural network architectures tailored to specific tasks
  • Acquire skills in employing optimization methods to train neural networks efficiently and effectively
  • Enhance problem-solving skills by applying deep learning concepts and techniques to real-world scenarios
MODULE 3
Convolutional Neural Networks
This module focuses on providing a comprehensive understanding of Convolutional Neural Networks (CNNs) for image analysis, with a specific emphasis on modern architectures. Participants will delve into the key principles that underpin CNNs and explore advanced architectures such as ResNet, VGG, and Inception. The module aims to equip learners with the knowledge and skills necessary to leverage state-of-the-art CNN architectures for enhanced computer vision performance in image analysis tasks.
LEARNING OUTCOMES
  • Understand the foundational principles of Convolutional Neural Networks (CNNs) and their significance in image analysis.
  • Gain insights into the architecture of modern CNNs, including the underlying mechanisms of convolutional layers, pooling layers, and fully connected layers.
  • Explore and analyze advanced CNN architectures, such as ResNet, VGG, and Inception, to comprehend their design principles and advantages.
  • Acquire the ability to select and implement appropriate CNN architectures based on specific image analysis requirements.
  • Develop proficiency in evaluating and comparing the performance of different CNN architectures in computer vision tasks.
  • Apply knowledge gained to solve real-world problems in image analysis using state-of-the-art CNN models.

KEY SKILLS
  • Explore and analyze advanced CNN architectures (ResNet, VGG, Inception) to understand their design principles and applications
  • Develop the ability to choose appropriate CNN architectures based on specific image analysis tasks
  • Evaluate and compare the performance of different CNN architectures in computer vision applications
Tools:
Pytorch
Jupyter Notebooks
Tools:
PyTorch
Image Processing Libraries
Module 4
Object Detection & Semantic Segmentation
This module focuses on the techniques of Localization and Object Detection, aiming to equip participants with the skills to locate and identify objects within images. The module covers the principles and implementation of object localization and detection algorithms. Additionally, the module delves into Semantic Segmentation, teaching participants how to segment images at the pixel level, providing detailed information about object boundaries.
LEARNING OUTCOMES
  • Gain practical experience in implementing object detection techniques.
  • Acquire the ability to segment images into pixel-level object categories.
  • Develop a deep understanding of the algorithms used in object localization, detection, and semantic segmentation.
  • Develop proficiency in coding and applying semantic segmentation techniques.
  • Learn how to evaluate the performance of localization, object detection, and semantic segmentation algorithms.

KEY SKILLS
  • Develop skills in locating and identifying objects within images using various detection algorithms
  • Acquire proficiency in segmenting images at the pixel level to identify object categories and boundaries
  • Gain a deep understanding of the underlying algorithms for object localization, detection, and semantic segmentation
  • Learn how to evaluate and assess the performance of localization, object detection, and semantic segmentation techniques
  • Apply localization, detection, and segmentation knowledge to solve real-world problems in image analysis
MODULE 5
Transfer Learning
This module focuses on Transfer Learning, Generative Models for Computer Vision (CV) with a specific emphasis on Style Transfer and Image Colorization, and an in-depth exploration of Generative Adversarial Networks (GANs). Participants will learn to leverage pre-trained models for computer vision tasks to save time and enhance model performance. Additionally, the module covers techniques for generating new images, transforming existing ones, and understanding the applications of GANs in generating images, videos, and more.
LEARNING OUTCOMES
  • Learn how to leverage pre-trained models to save time and improve the performance of computer vision tasks.
  • Acquire skills in generating new images and transforming existing ones using generative models.
  • Explore the various applications of GANs in generating images, videos, and other content in computer vision.
  • Learn how to implement transfer learning techniques and generative models for style transfer, image colorization, and GAN applications.
  • Learn about metrics used to assess the quality of generated images and videos.
KEY SKILLS
  • Develop proficiency in applying generative models for style transfer and image colorization
  • Learn how to implement Generative Adversarial Networks for generating images and videos
  • Learn how to evaluate the performance and quality of models generated through transfer learning and generative techniques
Tools:
PyTorch
GANs
Tools:
PyTorch
3D Computer Vision Libraries
MODULE 6
Augmentation Fine Tuning
This module covers Image Augmentation and Fine-Tuning techniques, providing participants with methods to enhance datasets and fine-tune models for improved performance in specific tasks. The module also explores 3D Object Detection, extending the scope of object detection to the three-dimensional space, enabling the detection of objects in real-world environments.
LEARNING OUTCOMES
  • Learn methods for fine-tuning models to achieve better results in specific computer vision tasks.
  • Understand how augmented datasets contribute to improved model generalization.
  • Learn how to adjust model parameters and optimize performance for target tasks.
  • Acquire skills in 3D object detection techniques for real-world environments.
  • Learn how to implement and fine-tune models for 3D object detection.
KEY SKILLS
  • Acquire skills in implementing image augmentation techniques to enhance dataset diversity
  • Gain practical knowledge of extending object detection to three-dimensional space
  • Learn how to adapt pre-trained models through fine-tuning for task-specific requirements
  • Develop the ability to evaluate model performance in real-world environments, considering the challenges of 3D object detection
FACULTY TEAM

Learn from the best

We’ve brought together a team of highly skilled and experienced instructors to help you learn effectively. Our instructors have a passion for teaching and a wealth of real-world experiences in their respective fields, so you can be confident that you’re learning from the best.

WeCloudData AI Bootcamp Instructor Farnoosh
Farnoosh Khodakarami
Machine Learning Researcher, Cyclica
Experienced computer scientist with a demonstrated history of working in the research industry. Skilled in application development with experience in machine learning applications. Strong research professional with a Doctor of Philosophy (Ph.D.) focused in Computer Science from the Amirkabir University of Technology – Tehran Polytechnic. Creative, self-motivated and committed to working with a team-player attitude, great problem-solving skills and ability to quickly grasp new concepts.
WeCloudData AI Bootcamp Instructor Ali Madani
Ali Madani
Machine Learning Team Lead, Cyclica
Ali is the head of machine learning at Cyclica Inc. and leads the team to further improve Cyclica’s deep learning technology for predicting interaction between ligands and target proteins. As a computational biologist and machine learning specialist, Ali has worked on a series of scientific articles in high impact scientific journals and international conferences covering such fields as transfer learning, dimensionality reduction and unsupervised clustering. He earned a Ph.D degree from the University of Toronto, and a master of a mathematics degree from the University of Waterloo.
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AI Course: Computer Vision
$3,500 USD
  • Case-based learning
  • Portfolio project mentoring
  • Flexible payment plan
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Recommended Short Courses
$3,500 USD
  • Enrich your AI experience with LLM and MLOps engineer courses
  • Get alumni discount for other DE, AI, and MLE courses
  • Short courses to consider after completing this course ⇩
Upgrade to Bootcamp
$10,000 USD
  • Upgrade to the AI/MLE bootcamp and get $5,000 discount
  • Get extensive 1-1 career mentoring and job support
  • Get the flexibility to create your own bootcamp
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What our graduates are saying

5/5

Jason Lee, Alumni

Thank you so much for coordinating an awesome course. The assistant instructor was really great in exposing to us how course material is applied in production level environment. I also like the instructor’s approach of pushing us to build from fundamental to real project using PyTorch first. And then progressing to Tensorflow. Personally, I think it’s a super awesome course, but I believe you really have to dig yourself into the contents and dedicate many head banging hours. But course material is very practical both in theory and application. Thanks much!

5/5

Waqas Khan, Senior Data Scientist

All the Instructors and TA’s are very knowledgeable and are always available for any clarification or support. There are dedicated TA office hours daily to assist students if there are any roadblocks in their assignments. Students are generally from very different backgrounds and experience levels but the Instructors and TA’s do a great job to make sure that everyone is following along and is on the same page.

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FAQ

Frequently asked questions about the bootcamp
To become successful in this course the learner will need to have solid python programming skills and experience with machine learning. Understanding of neural network and deep learning basics and familiarity with PyTorch or Tensorflow libraries will be useful. Knowing how to work with Numpy arrays and basic image processing skills using libraries such as OpenCV will be helpful as well.
Yes. This course focuses on the computer vision applications and use deep learning heavily. Learners will need to be comfortable with ML pipelines, training neural networks using PyTorch
Though the course is designed for anyone who wants to learn computer vision applications with deep learning, knowing how to process image files before attending this course will be very helpful. We recommend learners go through some prep materials and learn how to work with opencv. Basic data processing skills with Numpy and Pandas are also useful.
This course is very practical and focuses on implementation. It specifically focuses on the CNN (convolutional neural networks) models and various architectures. It also covers different use cases of computer vision such as image classification, image recognition, semantic segmentation, and generative models in CV. For generative models, we cover GANs and Diffusion Models and will focus more on how to apply them for different tasks.
We don’t offer extensive job support in our short courses. If you need career mentorship and help, you have two options. You can either enrol in the career mentorship program with alumni discount or consider joining our machine learning bootcamp. You will be able to get a $5000 scholarship for the Bootcamp and fully take advantage of the career support.
Depending on your existing skill sets and experience with machine learning, learners usually spend 15 to 20 hours each week (including the lectures and labs)
Yes, during the regular weeks we have office hours and labs sheets students get to follow labs and ask questions. During the project weeks, students will join the project mentoring sessions to interact the project mentors.
The project mentors will teach students the blueprint of building end to end computer vision project. Students are encouraged to have their own ideas and project use cases. Project can be fairly advanced depending on the time commitment.
Labs are designed to help learners practice what’s taught during the lectures. Instructors will be hands-on demos and cover new topics. Any questions regarding the lectures, demos will be addressed in the lab sessions. Students will be given additional lab exercises and self-paced exercises to work on. Lab instructors will provide live solution walkthroughs and students are encouraged to follow along and ask lots of questions. If the students have additional questions outside of the class, we encourage you to reach out to teaching assistants on Slack or attend the office hours.
This course is designed to be very hands-on. It’s impossible to become good at computer vision and deep learning without actually trying to build projects. If you prefer a more academic environment, we recommend you consider a Master’s program. If you want to gain practical experience and build portfolio projects, this is the perfect course.
We will cover how to process images using OpenCV and Torchvision. PyTorch and Huggingface will be used heavily for model training and tunings. Apart from the tools, learners will also need to know how to load and apply open source CV models such as stable diffusion, and how to work with GPUs.
There are two types of projects: personal projects (also called capstone or portfolio projects) and real client projects. All students in the course will need to complete the capstone project. The real client project is a different training and career service offered at WeCloudData via our partner Beamdata. Learners will become a trainee and receive project-based training. Learners will be assigned to a real project team to work with clients and get mentored and trained by our project managers and project leads. It’s a great learning opportunity and also allow the students to gain real experience to stand out in the job market. You can talk to our learning advisors to find more details.
Yes. Lots of labs and exercises. Students will have access to quizzes so they can test their knowledge on certain topics.
Yes, payment plan is available for this course. You can fill out the form on this course page to access the course package page. It has funding related information. Our learning advisor can also help you with your questions.
Yes, absolutely. All short courses are eligible for bundling and scholarship. When you purchase multiple courses you can get good discounts. Please reach out to our learning advisors by filling out the inquiry form.
No. This AI course is developed for anyone with solid python and ML experience. You don’t need to be a SDE or developer. Many students in this program are from data science and analytics background.
Yes. Scholarship is available for students who meet the requirements. A scholarship test needs to be completed and the learner needs have a 20-minute live assessment with the program manager. Alumni who have completed courses that meet the pre-requisites will also be eligible for scholarships.
This is a short course that’s part of the diploma program which is only available to learners in Canada, and you will need to complete 3 courses. Please contact our learning advisor for more details. For US and international learners, you will get a certificate by completing the course.
Courses in the AI/ML program usually have a good mix of learners from various background. Most students already have python programming and data science experiences. It doesn’t have to be related work experience though. We have learners who has completed self-paced ML courses or other data science bootcamps as well. Typically learners come from: 1. Data scientist who want to up-skill themselves. 2. Software engineers who want to learn how to build CV applications. 3. New graduates from computer science or computer engineering who have taken ML courses in school or who can complete the pre-course materials on their own. 4. Career switchers who take this course as part of the ML Engineering Bootcamp. 5. Non-tech background learners who have completed WeCloudData’s AI short courses as a pre-requisites. 6. PhD graduates who want to fast track their job search and meet the technical pre-requisites
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View our AI Course: Computer Vision course package
View our AI Course: Computer Vision course package