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(Urdu/Hindi ) Learn how to make an ML classifier without programming or installing anything.

By Taha Anwar

On January 25, 2020

Teachable Machine Version 1 (Google AI Experiments) 

In this video lesson I’ll teach you how to create an Image Classifier without actually coding. For this I’m using Teachable Machine version 1 which is part of Google AI Experiments. This application allows you to create image classifiers and introduces computer vision to new comers in a really fun and exciting way. I also go in the technical working of this application so people who already have some fundamental knowledge about about building classifiers can benefit from this.

Here’s the link to access this amazing tool. This is version 1, version 2 of this has also been released which deals with pose and voice recognition and even lets you export the models.

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

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

This is the last tutorial in our 3 part Creating Instagram Filters series. In this tutorial, you will learn to create 10 very interesting and cool Instagram filters-like effects on images and videos. The Filters which are gonna be covered are; Warm Filter, Cold Filter, Gotham Filter, GrayScale Filter, Sepia Filter, Pencil Sketch Filter, Sharpening Filter, Detail Enhancing Filter, Invert Filter, and Stylization Filter.

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