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Machine Learning Tutorial Python - 19: Principal Component Analysis (PCA) with Python Code
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Machine Learning Tutorial Python | Machine Learning For Beginners - Machine Learning Tutorial Python - 19: Principal Component Analysis (PCA) with Python Code

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  • 12 hours of video
  • Certificate of completion
  • Access on mobile and TV

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PCA or principal component analysis is a dimensionality reduction technique that can help us reduce dimensions of dataset that we use in machine learning for training. It helps with famous dimensionality curse problem. In this video we will understand what PCA is all about, write python code for handwritten digits dataset classification and then use PCA to train the same model using PCA. Code: https://github.com/codebasics/py/blob/master/ML/18_PCA/PCA_tutorial_digits.ipynb Exercise: https://github.com/codebasics/py/blob/master/ML/18_PCA/pca_exercise.md ⭐️ Timestamps ⭐️ 00:00 Theory 09:12 Coding 23:04 Exercise Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses. 🌎 My Website For Video Courses: https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description Need help building software or data analytics and AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website. 🎥 Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg #️⃣ Social Media #️⃣ 🔗 Discord: https://discord.gg/r42Kbuk 📸 Codebasics Instagram: https://www.instagram.com/codebasicshub/ 🔊 Codebasics Facebook: https://www.facebook.com/codebasicshub 📝 Codebasics LinkedIn: https://www.linkedin.com/company/codebasics/ 📱 Codebasics X handle: https://twitter.com/codebasicshub 📸 Dhaval's Instagram: https://www.instagram.com/dhavalsays/ 📝 Dhaval's LinkedIn: https://www.linkedin.com/in/dhavalsays/ 📱 Dhaval's X handle: https://x.com/dpcodebasics ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

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