​Welcome to Machine Learning Mindset

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A Message from the Founder

Thank you so much for joining our journey to mastering AI and Machine Learning. My name is Amirsina Torfi and I dedicated my professional life to open source and personally have been ranked by git-awards as one of the top 20 GitHub Python developer in the USA and top 100 worldwide. The GitHub repository associated with this blog is itself ranked as the top 300 GitHub accounts worldwide.This is NOT because I am a good coder! NOT at all! There are many developers out there doing that better than me for sure! Because I know how to build projects and how to educate others to do the same. That is what you will get joining us! Let's get started! You can check my personal works through the following links:

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What's Next?

Below, you see the some sample projects and blog posts that we developed.You can alternatively visit OUR BLOG, to dig into our published articles.


TensorFlow Course

This repository aims to provide simple and ready-to-use tutorials for Google TensorFlow which is one of the most famous Machine Learning libraries. Currently ranked as #9 Github project in TensorFlow worldwide. This project has been the GitHub trending repository of the day, week, and month.


Python Machine Learning Course

We've created this free Machine Learning book to teach you the Machine Learning and Deep Learning core concepts aims to provide the fundamental knowledge for non-experts that are interested in mastering Machine Learning. This project has also been the GitHub trending repository of the day, week, and month.


What is Machine Learning?

The purpose of this article is to provide an insightful overview of Machine Learning by presenting a high-level definition of that and further break it into its associated categories.


Understand the Powerful Supervised Machine Learning in a Simple Way

In this article, we focus on the notion of supervised machine learning and its associated categories. In addition, we describe the components of supervised learning. Finally, we briefly discuss the popular categories and algorithms without digging too much into details.

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