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Admin: @PranavReal Free Resources for: 📌 Artificial Intelligence 📌 Machine Learning 📌 Deep Learning 📌 Data Science 📌 Python Programming

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NVIDIA GTC is the most important conference for the era of AI and the metaverse. Join us online as we explore the innovations
NVIDIA GTC is the most important conference for the era of AI and the metaverse. Join us online as we explore the innovations that will impact your life’s work with the power of AI, computer graphics, data science, and more. The conference will run from September 19 -22, it is fully virtual and free to attend. Let innovation inspire your next idea, or solve your biggest challenge. With talks delivered by pioneers in their fields to relatable use cases and Deep Learning Institue training where you can gain NVIDIA certification, to Watch Parties where you can engage with your peers, you can be part of what comes next at GTC. Register today

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Machine Learning Notes - TutorialsDuniya.pdf14.65 MB

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Master the art of Machine Learning and python by Learning from Real World Advanced Projects designed & developed by real developers. 👨🏻‍🎓Are you one who knows the basics of Python/Machine Learning/Deep Learning/ Data Science/ Computer Vision or NLP but has not practiced on Real World Advanced Projects? 👨🏻‍🎓Are you in the final year of your college but have not made any real-world projects till now? Or are you a professional who wants to switch to a Data-Oriented job but does not have any real-world projects to showcase in your resume? Don’t worry dude... We have something for you. This Dragon bundle is divided into two modules, 📌Module 1: 350+ Machine Learning Projects. 📌Module 2: 200+ Advanced Python Projects. ➡️ Get the complete list of projects from our website (Link is in the bio). ⚫But Why? ➡️ Learn by working like a professional developer. ➡️ Build a GitHub portfolio that impresses the recruiters. ➡️ Stand out with real proof of work. ➡️ It's more than 550+ real-world projects, almost in every area you are going to work in the field of Machine Learning. All Projects come with source code in Python3 and dataset. Also, each project comes with proper documentation, requirements, and the HowtoRun instructions file so that even beginners can understand the logic and workflow behind the projects. For more details, visit our website. WE HAVE CHANGED OUR DOMAIN NAME FROM TENSORPROJECTS.COM TO TENSORCODE.IO. ⏩ Link is in bio.

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Master the art of Data Science and python by Learning from Real World Advanced Projects designed & developed by real developers. 󰞪Are you one who knows the basics of Python/Machine Learning/Deep Learning/ Data Science/ Computer Vision or NLP but has not practiced on Real World Advanced Projects? 󰞪Are you in the final year of your college but have not made any real-world projects till now? Or are you a professional who wants to switch to a Data-Oriented job but does not have any real-world projects to showcase in your resume? Don’t worry dude... We have something for you. This Dragon bundle is divided into two modules, 📌Module 1: 350+ Machine Learning Projects. 📌Module 2: 200+ Advanced Python Projects. ➡️ Get a complete list of projects from our website (Link is in bio). ⚫But Why? ➡️ Learn by working like a professional developer. ➡️ Build a GitHub portfolio that impress the recruiters. ➡️ Stand out with real-proof of work. ➡️ Its more than 550+ real-world projects, almost in every areas you are going to work in the field of Machine Learning. All Projects come with source code in Python3 and dataset. Also, each project comes with proper documentation, requirements, and the HowtoRun instructions file so that even beginners can understand the logic and workflow behind the projects. For more details, visit our website. Link: https://tensorprojects.com/dragonbundle/.

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You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why? Answer: This question has enough hints for you to start thinking! Since, the data is spread across median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

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