8 Best Computer Vision Courses For Beginners in 2021

Explore how you can use computer vision to develop applications that can recognize and classify objects in images and video. Start with the best courses and learn Computer Vision from scratch.

8 Best Computer Vision Courses For Beginners in 2021

Computer Vision includes methods for capturing, processing, analyzing, and understanding digital images, as well as extracting high-dimensional data from the real world to generate numerical and symbolic information.

At its core, Computer Vision focuses on the development of computer systems that are capable of capturing, understanding, and interpreting important visual information in images and video data.

Computer vision focuses on replicating some of the complexity of human vision systems and enabling computers to recognize and process objects in images and videos in a similar way to humans.

Keeping this in mind, here at Coursesity, we have curated some of the Best Online Professional Scrum Master courses with certification. Hope that you will find the best course for you to learn how to replicate some of the complexity of human vision systems and enable computers to recognize and process objects in images and videos in a similar way to humans.

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Top Computer Vision Courses List

  1. Autonomous Cars: Deep Learning and Computer Vision in Python

  2. Introduction to Computer Vision and Image Processing

  3. Python for Computer Vision with OpenCV and Deep Learning

  4. Advanced Computer Vision with TensorFlow

  5. Master Computer Vision™ OpenCV4 in Python with Deep Learning

  6. OpenCV for Python Developers Online Class

  7. Computer Vision and Image Processing Fundamentals

  8. Computer Vision and Image Analysis - Online Course

1. Autonomous Cars: Deep Learning and Computer Vision in Python

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars.

Course rating: 4.8 out of 5.0 ( 861 Ratings total)

Duration: 21 h

Certificate: Certificate on purchase

In this course, you will:

  • Automatically detect lane markings in images.
  • Detect cars and pedestrians using a trained classifier and with SVM.
  • Classify traffic signs using Convolutional Neural Networks.
  • Identify other vehicles in images using template matching.
  • Build deep neural networks with Tensorflow and Keras.
  • Analyze and visualize data with Numpy, Pandas, Matplotlib, and Seaborn.
  • Classify data with machine learning techniques including regression, decision trees, Naive Bayes, and SVM.
  • Classify data with artificial neural networks and deep learning.

This Computer Vision course provides you with practical experience in various self-driving vehicles concepts such as machine learning and computer vision. Concepts such as lane detection, traffic sign classification, vehicle/object detection, artificial intelligence, and deep learning will be presented.

Next, you will learn how to gain a fundamental understanding of self-driving vehicles control. By the end of this course, you will master driverless car technologies that are going to reshape the future of transportation.

You can take Autonomous Cars: Deep Learning and Computer Vision in Python certificate course on Udemy.

2. Introduction to Computer Vision with Watson and OpenCV

Learn Introduction to Computer Vision with Watson and OpenCV from IBM.

Course rating: 4.5 out of 5.0 ( 662 Ratings total)

Duration: 21 h

Certificate: Certificate on purchase

In this course, you will learn:

  • Object detection with different methods.
  • How to use the Haar Cascade classifier, R-CNN, and MobileNet.
  • Different components such as Layers and different types of activation functions such as ReLU.
  • CNN Architectures such as ResNet and LenNet.
  • Building a computer vision app that you will deploy on the cloud through Code Engine.

Initially, this Computer Vision course will discuss the rapidly developing field of image processing. In addition to being the first step in Computer Vision, it has broad applications ranging anywhere from making your smartphone's image look crystal clear to helping doctors cure diseases.

Next, you will learn the basics of image processing with Python libraries OpenCV and Pillow. You will also learn about the different Machine learning classification Methods commonly used for Computer vision, including k nearest neighbors, Logistic regression, SoftMax Regression, and Support Vector Machines.

Finally, you will learn about Neural Networks, fully connected Neural Networks, and Convolutional Neural networks (CNN).

You can take Introduction to Computer Vision with Watson and OpenCV certification course on Coursera.

3. Python for Computer Vision with OpenCV and Deep Learning

Learn the latest techniques in computer vision with Python, OpenCV, and Deep Learning!

Course rating: 4.6 out of 5.0 ( 7,059 Ratings total)

Duration: 14 h

Certificate: Certificate on purchase

In this course, you will learn:

  • Understand the basics of NumPy.
  • Manipulate and open Images with NumPy.
  • Use OpenCV to work with image files.
  • Use Python and OpenCV to draw shapes on images and videos.
  • Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
  • Create Color Histograms with OpenCV.
  • Open and Stream video with Python and OpenCV.
  • Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python.
  • Use Python and Deep Learning to build image classifiers.
  • Work with Tensorflow, Keras, and Python to train on your own custom images.

You will start this Computer Vision course by learning about numerical processing with the NumPy library and how to open and manipulate images with NumPy. Then will move on to using the OpenCV library to open and work with image basics.

Next, you will understand how to process images and apply a variety of effects, including color mappings, blending, thresholds, gradients, and more. You will also learn about video basics with OpenCV, including working with streaming video from a webcam.  

Plus, you will learn about direct video topics, such as optical flow and object detection. Including face detection and object tracking.

You can take Python for Computer Vision with OpenCV and Deep Learning certificate course on Udemy.

4. Advanced Computer Vision with TensorFlow

Learn Computer Vision from scratch. Offered by DeepLearning.AI.

Course rating: 4.8 out of 5.0 ( 257 Ratings total)

Duration: 29 h

Certificate: Certificate on completion

In this course, you will:

  • Explore image classification, image segmentation, object localization, and object detection.
  • Apply transfer learning to object localization and detection.
  • Apply object detection models such as regional-CNN and ResNet-50.
  • Customize existing models and build your own models to detect, localize, and label your own rubber duck images.
  • Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net.
  • Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet.

With this Computer Vision course, you will learn how to apply TensorFlow to build object detection and image segmentation, models. The course will give you an overview of some popular object detection models, such as regional-CNN and ResNet-50.

Next, you will use object detection models that you’ll retrieve from TensorFlow Hub, download your own models and configure them for training, and also build your own models for object detection.

Moreover, you will learn all about image segmentation using variations of the fully convolutional neural network. With these networks, you can assign class labels to each pixel, and perform much more detailed identification of objects compared to bounding boxes.

You can take Advanced Computer Vision with TensorFlow certification course on Coursera.

5. Master Computer Vision™ OpenCV4 in Python with Deep Learning

Master OpenCV4 like a pro while learning Dlib, Deep Learning Computer Vision (Keras, TensorFlow & Caffe)

Course rating: 4.4 out of 5.0 ( 3,461 Ratings total)

Duration: 10 h 44 m

Certificate: Certificate on purchase

In this course, you will:

  • Understand and use OpenCV4 in Python.
  • Use Deep Learning using Keras & TensorFlow in Python.
  • Create Face Detectors & Recognizers and create your own advanced face swaps using DLIB.
  • Learn Programming skills such as basic Python and Numpy.
  • Use Computer Vision in executing cool startup ideas.
  • Understand Neural and Convolutional Neural Networks.
  • Build simple Image Classifiers in Python.
  • Perform Neural Style Transfer Using OpenCV.
  • Learn the Basics of Computer Vision and Image Processing.

With this Computer Vision course, you will discover the power of OpenCV in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer. You will learn about key concepts of Computer Vision & OpenCV (using the newest version OpenCV4)

Next, you will learn about Image manipulations such as transformations, cropping, blurring, thresholding, edge detection, and cropping. You also learn about the Segmentation of images by understanding contours, circle, and line detection.

You will even learn how to approximate contours, do contour filtering and ordering as well as approximations.

You can take Master Computer Vision™ OpenCV4 in Python with a Deep Learning certificate course on Udemy.

6. OpenCV for Python Developers Online Class

Learn how to use the image-processing power of OpenCV 3 to add an object, facial, and feature detection to your Python applications.

Course rating: 131,894 total enrollments

Duration: 2 h 35 m

Certificate: Certificate on completion

The course includes:

  • Install and Configure OpenCV
  • Basic Image Operations
  • Object Detection
  • Face and Feature Detection

This Computer Vision course offers a detailed introduction to OpenCV 3, starting with installing and configuring your Mac, Windows, or Linux development environment along with Python 3.

You will learn about the data and image types unique to OpenCV, and find out how to manipulate pixels and images. The course also shows how to read video streams as inputs and create custom real-time video interfaces.

Plus, you will learn about the object, facial, and feature detection. Learn how to leverage the image-processing power of OpenCV using methods like template matching and machine learning data to identify and recognize features.

You can take OpenCV for Python Developers Online Class certification course on Linkedin Learning.

7. Computer Vision and Image Processing Fundamentals

Learn about computer vision, one of the most exciting fields in machine learning. artificial intelligence and computer science.

Course rating: 12,543 total enrollments

Duration: 9 h

Certificate: Certificate on completion

In this course, you will learn:

  • Various computer vision applications across many industries.
  • Imaging processing and formation capabilities powered by AI.
  • Utilize Python, Watson AI, and OpenCV to process images and interact with image classification models.
  • Build, train, and test your own custom image classifiers.

In this course, you will learn about computer vision and its various applications across many industries. As part of this course, you will utilize Python, Watson AI, and OpenCV to process images and interact with image classification models. You will also build, train, and test your own custom image classifiers.

By the end of the Computer Vision course, you will create your own computer vision web app and deploy it to the Cloud.

You can take Computer Vision and Image Processing Fundamentals certification course on Edx.

8. Computer Vision and Image Analysis - Online Course

Learn how computer vision is important in AI and gain practical experience of image analysis.

Duration: 20 h

Certificate: Certificate on completion

In this course, you will:

  • Apply classical Image Analysis techniques, such as Edge Detection, Watershed, and Distance Transformation as well as K-means Clustering to segment a basic dataset.
  • Implement classical Image Analysis algorithms using the OpenCV library.
  • Compare classical and Deep-Learning object classification techniques.
  • Apply Microsoft ResNet, a deep Convolutional Neural Network (CNN) to object classification using the Microsoft Cognitive Toolkit.

During this Computer Vision course, you will learn all about Image Analysis techniques and why computer vision is important in AI. You’ll explore classical Image Analysis techniques such as Edge Detection, Watershed, and Distance Transformation, as well as K-means clustering to increase your knowledge on this AI component.

Plus, you will learn the evolution of Image Analysis to understand the background of this field of AI. By the end of the course, you’ll be able to compare classical and deep learning object classification techniques and apply them to modern AI technologies.

You can take Computer Vision and Image Analysis - Online Course certification course on Futurelearn.


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