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(Urdu/Hindi) From Training to Deploying Classification, Pose Estimation & Sound Recognition models without coding.

By Taha Anwar

On April 4, 2020

In less than 10 minutes I teach you how to train an effective hand finger recognition classifier, a pose detection model & a sound recognition model and also show you multiple deployment options.

If you’re not impressed yet than let me tell you this: “You won’t need any programming knowledge or need to install anything to work with this, an internet connection with a browser is sufficient.”

Unless you’re using Internet Explorer 😐

So the tool we are using is Teachable Machine version 2.  A few months ago I made a video on Teachable machine version 1 but version 1 was more of a teaching tool. This version actually is a lot more powerful and allows you to export models in various ways.

I’m offering a premium 3-month Comprehensive State of the Art course in Computer Vision & Image Processing with Python (Urdu/Hindi). This course is a must take if you’re planning to start a career in Computer vision & Artificial Intelligence, the only prerequisite to this course is some programming experience in any language.

This course goes into the foundations of Image processing and Computer Vision, you learn from the ground up what the image is and how to manipulate it at the lowest level and then you gradually built up from there in the course, you learn other foundational techniques with their theories and how to use them effectively.

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Designing Advanced Image Filters in OpenCV | Creating Instagram Filters – Pt 3⁄3

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10 Comments

  1. Hayat Ullah

    Much appreciated your work..
    thanks you sir.. God bless you..
    Plz keep on sharing these valuable information regarding CV and AI..

    Reply
    • Taha Anwar

      Thank you Hayat for the comments.

      Reply
  2. Muhammad Aqib

    Thank you sir.Allah ap ko khosh rake.

    Reply
    • Taha Anwar

      Thank you Aqib

      Reply
  3. Asif

    I really appreciate your work. Indeed you are doing a tremendous job. Quality Content

    Reply
    • Taha Anwar

      Much Appreciated, thank you.

      Reply
  4. Usama Habib

    Keep up the good work sir much appreciated

    Reply
    • Taha Anwar

      Thank you Usama.

      Reply
  5. Rao Tariq

    Commendable performance. Thanks a lot

    Reply
    • Taha Anwar

      You’re Welcome 🙂

      Reply

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