How can you start learning computer vision with no experience?

Begin by grasping foundational concepts in linear algebra, calculus, and programming languages like Python. Familiarize yourself with libraries such as OpenCV, TensorFlow, or Pytorch. Engage in online courses, like those on Coursera or edX, offering comprehensive insights into computer vision. Practice is paramount; work on small projects to hone your skills. Leveraging online communities, such as GitHub and Stack Overflow, will enhance your learning through shared experiences and collaborative problem-solving.

Start with the fundamentals of Image processing, programming in Python and AI algorithms. For this, there are many globally recognized MOOCs available free (with nominal charges). Having good knowledge of programming logics and concepts, average mathematical problem solving skills, good creative thinking and visualization capability, basic understaing of Python script syntax and certain libraries like Open CV, Keras, Scikit learn, matplotlib etc. may be a good start. Next step may be to learn the computer vision & pattern recognition methods and solve related problems using Python scripts. Try to reimplement the already solved computer vision problems by your own to boost up your confidence and later, try some innovative problems to solve.

To learn the basic for computer vision , I would recommend the following 1. Understand linear algebra, calculus, probability, and statistics. 2. Learn Python and libraries like OpenCV, PyTorch, TensorFlow, or Keras. 3. Study basic techniques like filtering and feature extraction. 4. Gain knowledge in supervised and unsupervised learning, classification, regression, clustering 5.Deep Learning - Learn about convolutional neural networks (CNNs). 8. **Stay Updated**: Keep abreast of the latest research and advancements in the field.

Learning computer vision from scratch may seem daunting, but it's totally doable! Start by grasping the basics of math and coding. Online courses like Khan Academy offer free tutorials on algebra and calculus, while platforms like Codecademy teach Python programming. Once you're comfortable with these fundamentals, dive into computer vision-specific resources. Websites like OpenCV provide beginner-friendly guides and tutorials, and YouTube channels like Sentdex offer step-by-step video tutorials. Practice coding simple projects like image classification or object detection to reinforce your learning. With dedication and practice, you'll soon be on your way to mastering computer vision.

- Gain a basic understanding of deep learning techniques, such as convolutional neural networks - Utilize resources such as books, online courses, or tutorials to learn about computer vision - Build a strong foundation in programming, particularly in languages such as Python - Acquire knowledge of linear algebra and image processing - Start with simple projects and gradually work towards more complex ones - Stay updated on the latest advancements and techniques in computer vision - Network with other professionals in the field - Attend conferences or workshops related to computer vision.

To start learning computer vision without experience, grasp foundational math concepts like linear algebra and calculus, then learn Python programming. Familiarize yourself with key libraries such as NumPy and OpenCV. Dive into image processing techniques, exploring tasks like edge detection and feature extraction. Understand core computer vision concepts like object detection and classification. Engage in hands-on projects to apply your knowledge and build practical skills.

Starting with Image Formation/Processing concepts would lay a good theoretical foundation. Then one can proceed to learn feature detection & matching. Working on a few basic projects on the above concepts using OpenCV will give a hands-on experience. This will be a from "Zero to One" move...additionally learning a few Deep Learning concepts like CNN will take your journey forward. Some useful resources are: 1. Theory : "Computer Vision: Algorithms and Applications" by Richard Szeliski 2. Hands-on : PyImageSearch by Adrian Rosebrock

Here's a roadmap to get you started with computer vision, even with no prior experience: 1. Build a Foundational Understanding: Math and Programming: Brush up on your high school math (linear algebra, calculus) and basic programming skills (Python is a popular choice). Online resources like Khan Academy or Coursera offer free courses. Essential Tools: Familiarize yourself with Python libraries like NumPy (numerical computing) and Matplotlib (data visualization). These libraries will be crucial for working with computer vision data. 2. Explore Introductory Courses: Online Platforms: Platforms like Coursera, edX.

To start learning computer vision with no prior experience, begin by understanding basic concepts like pixels, color spaces, and filters. Learn programming languages like Python and relevant libraries such as OpenCV and TensorFlow. Enroll in beginner-friendly online courses, read beginner-friendly books, and explore online resources. Practice with small projects like image classification and object detection. Join online communities, contribute to open-source projects, and attend workshops/webinars to gain practical experience and stay updated with the latest trends. Continuous learning, experimentation, and engagement are key to building proficiency in computer vision.

Additional Considerations: Physics & Optics: Understanding light fundamentals and camera models can enhance your understanding of image formation and processing. Signal Processing: Knowledge of frequency analysis and filtering techniques can be valuable for image denoising and feature extraction. Machine Learning: This forms the heart of many computer vision algorithms, so familiarity with concepts like supervised learning, unsupervised learning, and neural networks is highly beneficial.

Start with openCV. But, do not "just use it", try to understand about each technique you going to try! Probably you need to learn a bit about neural networks, but not only AI... just try an exercise using openCV and go to find information abou the technique it uses!

Start with a beginner-friendly framework like TensorFlow or PyTorch, leveraging their documentation and community for learning computer vision from scratch.

When selecting a framework for computer vision projects, consider options like OpenCV, TensorFlow, PyTorch, Keras, and Scikit-learn. Each framework offers unique advantages and drawbacks, so choose based on your specific requirements and preferences. You can utilize multiple frameworks based on task demands; for instance, leverage OpenCV for image processing and manipulation, while employing TensorFlow for deep learning and neural networks. This approach ensures flexibility and optimization tailored to diverse computer vision tasks.

Surprisingly Azure is not mentioned as the platforms you can use! Azure AI Vision offers a innovative unified service that provides computer vision capabilities. Give your apps the ability to analyze images, read text, and detect faces with prebuilt image tagging, text extraction with optical character recognition (OCR), and responsible facial recognition. Incorporate vision features into your projects with no machine learning experience required.

When selecting a framework for computer vision, consider the project requirements and your familiarity with the language it supports. OpenCV is excellent for real-time image processing, while TensorFlow and PyTorch offer extensive resources for deep learning tasks. Keras, with its high-level API, can simplify neural network construction, and Scikit-learn is ideal for traditional machine learning algorithms. Combining frameworks can leverage their unique strengths, enhancing the capability and efficiency of your computer vision solutions.

OpenCV is excellent for real-time computer vision tasks and has a vast array of functions for image processing. TensorFlow and PyTorch are more suited for deep learning applications due to their dynamic computation graphs and support for GPU acceleration. Keras, which now integrates with TensorFlow, offers a high-level, user-friendly API for neural networks. Scikit-learn, while not specifically designed for deep learning, is a great choice for traditional machine learning algorithms. It's important to consider the specific requirements of your project and your familiarity with Python when selecting a framework.

Selecting a framework is pivotal for implementing computer vision algorithms effectively. Options like OpenCV, TensorFlow, PyTorch, Keras, and Scikit-learn offer diverse capabilities. Consider factors like ease of use, community support, and compatibility with your project requirements when making your choice. You can also experiment with multiple frameworks to gain a deeper understanding of their strengths and limitations before committing to one.

Choosing the right computer vision framework depends on your project needs and experience level. For beginners and simple image processing tasks, OpenCV is widely recommended due to its extensive documentation and ease of use. If your focus is on deep learning, TensorFlow offers a robust and scalable environment, while PyTorch is favored for its flexibility in research and development. Keras, running on top of TensorFlow, provides a more user-friendly interface for deep learning, making it ideal for those new to the field. FastAI, built on PyTorch, simplifies the process further, enabling quick prototyping with modern best practices.

There are many great free resources on Microsoft Learn where you can learn and try out basics. With easy-to-learn web-based services you can easily train your AI model for tasks such as classification or object detection. Achieving first visual results will motivate you to move forward, dig deeper into more advanced courses, and expand your knowledge in this area.

Following a structured and comprehensive course is essential for learning computer vision effectively. Several online courses cater to different levels and objectives. For beginners, Coursera's "Computer Vision Basics" covers fundamental concepts like image representation, filtering, and segmentation using OpenCV. Udemy's "Deep Learning for Computer Vision" delves into advanced techniques such as convolutional neural networks and object detection with TensorFlow and Keras. For more advanced learners, Udacity's "Computer Vision Nanodegree" explores topics like feature extraction, object tracking, and facial recognition using PyTorch.

Absolutely you can. Microsoft Learn has a number of valuable labs including an entire segment of the Azure AI-102 "AI Engineer" certification course where you can use an online lab environment without having to pay for any Azure credits to build a Custom Computer Vision (ML) solution as well as a no-code Computer Vision solution, with the benefit of working towards your certification. Highly recommended!

I advocate for following a structured course in computer vision. It offers a guided pathway through the basics to more advanced concepts, ensuring a solid foundation and systematic learning.

Computer vision is a multidisciplinary field that intersects with areas such as machine learning, signal processing, and neuroscience. When starting with no experience, it's crucial to build a strong foundation in both the theoretical underpinnings and practical applications. Courses like those mentioned provide a guided pathway from basic concepts to more complex techniques. The use of frameworks like OpenCV, TensorFlow, and PyTorch is essential for hands-on learning, as they are industry-standard tools that offer a wide range of functionalities for image processing and neural network implementation.

After covering the basics get enroll in a course. There are various courses of computer vision available online in famous platform including coursera,udemy,edx. On coursera computer vision course offered by deeplearning.ai is very good. Also you can learn from youTube. One youTube there are many lectures present like deep learning for computer vision offered by Standford or university of michigan. Udacity's nano degree course is also very cool here covering major topics from basics to advance.

For the third step, enroll in a structured course tailored to computer vision. Choose one that covers both theory and practical applications, from basic image processing techniques to advanced deep learning concepts. Look for courses with hands-on projects, as they provide valuable experience with real-world problems. Some platforms offer courses developed by universities and industry leaders, ensuring a comprehensive learning experience. These courses often include peer discussions and assignments, enhancing your understanding through collaboration and feedback. A well-chosen course accelerates your learning curve, bridging the gap between foundational knowledge and applied skills in computer vision.

Computer vision is a rapidly evolving field that intersects with machine learning and artificial intelligence. A structured course can provide a solid foundation, but it's important to supplement theoretical knowledge with practical experience. For beginners, learning the basics of image processing and familiarizing oneself with tools like OpenCV is crucial. As learners progress, delving into deep learning frameworks such as TensorFlow and Keras is essential for modern computer vision applications. Advanced courses like Udacity's Nanodegree offer exposure to cutting-edge techniques and real-world projects, which are invaluable for building a robust portfolio and understanding the nuances of this complex field.

The best project to start working on is one that aligns with your interests and challenges you at a comfortable level. For tech beginners, a simple web or mobile app, like a to-do list, offers a practical introduction to coding and design principles. Data enthusiasts might explore analyzing a public dataset to learn about data cleaning and basic analytics. Web development learners can benefit from building a responsive website, learning HTML, CSS, and JavaScript. For those interested in AI or machine learning, a project using pre-trained models for tasks like image classification can introduce key concepts without overwhelming complexity.

Dive into projects. It's a hands-on way to apply what you've learned, tackle real-world problems, and build a portfolio that showcases your skills in computer vision.

After learning the basics and doing course. Here's the important step and most effective step comes building valuable projects. Building projects is the best way to learn new techniques. First decide what project you want to built like want to built object detection project then collect the data annotate it the best platform I know for that is cvat.ai or you can also collect already annotated data from kaggle or roboflow. After that train the model on the annotated data and evalaute it. For training yolo is popular framework. You can get its details form ultralytics website.

Kaggle is a great option to get experience and to get information. Is a place you can find: datasets, projects, code, discussions, courses and competitions. Some of the competitions have Prizes & Awards. You can create / participate in a team for the competitions.

Many suggest starting with portfolio projects but do not to mention how to do that. Great resource and platform to learn by doing is RoboFlow(.)com. It’s full of projects, source code and access to inference. All things you’ll need to get started!

Engaging in practical projects is indeed a cornerstone of learning in AI. By tackling projects that range from simple to complex, learners solidify their understanding and gain invaluable problem-solving skills. Datasets like MNIST and CIFAR-10 serve as excellent starting points due to their simplicity and well-documented benchmarks. As learners progress, they can contribute to open-source projects or Kaggle competitions, which provide real-world challenges and community feedback, further enhancing their learning curve and portfolio.

Engaging in hands-on projects is indeed a powerful way to grasp computer vision concepts. Starting with simple tasks allows for foundational skill-building, while tackling more complex projects leads to a deeper understanding of advanced algorithms and their applications. Furthermore, utilizing publicly available datasets and challenges not only provides a wealth of data to train and test models on but also exposes learners to real-world problems that the AI community is actively trying to solve. This practical experience is invaluable and often sought after by employers in the AI field.

Engaging in practical projects is paramount for mastering computer vision. By applying theoretical knowledge to real-world scenarios, you solidify your understanding and enhance your skills. Start with basic projects like image classification or color detection, then progress to more intricate tasks such as face recognition or optical character recognition. Utilize online datasets and challenges like MNIST or CIFAR-10 to enrich your projects and challenge yourself further. Remember, each project is an opportunity to grow and refine your expertise in the dynamic field of computer vision.

In my experience, the best way to learn computer vision is through hands-on experience. So, a beginner project (such as MNIST classification) or the many tutorials online can be good places to start. Gradually, as you get more comfortable with the frameworks and libraries, you can start working on more complex problems.

I once engaged myself on a journey to produce a system capable of classifying garbage for a smart trash can! There is nothing more powerful like an idea to get you started on AI! You just need to pick one and have fun! Bonus tip! Try to find a learning buddy! Someone like you that will be shoulder by shoulder in the same journey!

Integrating into a community is vital. Engage with online forums, social media groups, and attend webinars or virtual meetups. Platforms like GitHub, Reddit, and LinkedIn are great for connecting with experts and peers. This interaction boosts learning, provides support, and keeps you updated on the latest in computer vision. Networking opportunities can also arise, enhancing both knowledge and career prospects.

Joining a community is crucial in the AI field, not only for support but also for staying updated with the latest advancements and trends. Engaging with a community provides practical insights that complement academic knowledge, fostering a more holistic understanding of computer vision. Moreover, networking within these communities can lead to collaborative opportunities and potential career advancements.

Join a community to navigate the computer vision learning journey. Engage with online forums, groups, and resources like Reddit, Stack Overflow, and Medium. Follow Computer Vision News and the Computer Vision Foundation. Attend events like CVPR, ICCV, and ECCV to connect with peers and experts, gaining inspiration and support. This approach enriches your learning experience, providing motivation and insights from the computer vision field.

If you have a good background on math, I would highly recommend to follow Stanford's Deep Learning for Computer Vision online courses. Led by Fei-Fei Li, it is an awesome deep dive into the details on how Computer Vision works.

Be mindful of the biases that AI could bring into your project! Always validate the sources that have been used to train the models you use! Making AI ethical depends on every single one of us everyday!

Like other areas of machine learning, computer vision faces similar challenges. As you embark on such a project, keep the following in mind. 1️⃣ You'll need a GPU. Plus different strategies to balance memory with batch size 2️⃣ Data quality of generally low 3️⃣ Missing or mis-labelled images 4️⃣ Unbalanced dataset 5️⃣ Unsafe use of image augmentation to increase test data size 6️⃣ Challenging images, E.g., too much contrast, poor colour balance, too much noise, etc Regardless of framework, keep these in mind.

Starting to learn computer vision with no experience can be daunting, but it's important to remember that practical experience is invaluable. While following courses and tutorials, try to apply what you learn to real-world problems. This could be as simple as writing a program to filter or detect edges in an image, or as complex as building a facial recognition system. Additionally, understanding the math behind algorithms can greatly enhance your ability to innovate and troubleshoot. Lastly, don't overlook the importance of soft skills such as problem-solving and critical thinking, which are crucial in the field of AI.

1. Choose a use case that genuinely excites you. Example, if autonomous vehicles fascinate you, that can be your starting point. 2. Before diving into project, get familiar with the foundational concepts of computer vision through online learning platforms. 3. Based on your interest, define a simple project. For autonomous cars, it could be something like detecting and classifying road signs using images. 4. Then build a simple model, using platforms like Hugging Face or PyTorch. 5. Look for datasets relevant to your project on websites like Kaggle. 6. Engage with communities on Stack Overflow or Reddit for support. 7. Iterate on your project, gradually tackling more complex problems. 8. Document your progress to solidify your learning.

To begin learning computer vision with no prior experience, start by familiarizing yourself with programming fundamentals, particularly in languages like Python, which is widely used in computer vision applications. Next, delve into introductory courses or online tutorials specifically tailored to computer vision concepts and libraries such as OpenCV and TensorFlow. Hands-on projects, including basic image manipulation and feature extraction, will solidify your understanding.

Computer vision is a very vast field, in terms of industries it serves, technologies is holds and the potential it carries. I'd start by reviewing the available avenues, like - healthcare, agriculture, security etc, and what techniques each can teach me (DL, classic, noise reduction, and so much more). Then, I'd try to find what's most appealing and see which direction is most appealing to me. Basic courses can provide a glimpse to what can and is being done, with multiple examples on github and online. A university degree provides theoretical knowledge and also the basics to help change direction so I'd strongly consider it.

To begin learning computer vision with no prior experience, start with basic tutorials on image processing and Python programming. Master libraries like OpenCV and TensorFlow. Dive into theory through online courses and books. Practice by implementing simple projects, gradually advancing to more complex tasks. Stay updated with research papers and join online communities for support and collaboration. Consistent practice and determination are key.