Data Science in 30 Days | Data Science Full Course Free | #datascience #fullcourse - Dimensionality Reduction: PCA & t-SNE Explained with Python | Day 15 | Data Science in 30 Days #data

Welcome to Day 15 of our Data Science in 30 Days course — exclusively on The Data Key! In this session, we dive deep into Dimensionality Reduction, one of the most powerful techniques in Machine Learning. You’ll learn how PCA (Principal Component Analysis) and t-SNE (t-distributed Stochastic Neighbor Embedding) help simplify data while keeping the most important information. ------------------------------------------------------------------------------------------------------------------------- 📚 In This Video You'll Learn: ✅ Introduction to Dimensionality Reduction ✅ Why Dimensionality Reduction is Needed ✅ PCA Intuition (Variance, Eigenvectors, Eigenvalues) ✅ PCA in Python (using Iris Dataset) ✅ Visualizing PCA Components ✅ Understanding t-SNE (and its differences from PCA) ✅ Implementing t-SNE with Scikit-learn ✅ PCA vs t-SNE Comparison ✅ When to Use Which ------------------------------------------------------------------------------------------------------------------------- 💻 Resources & References: 📘 Scikit-learn PCA Docs: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html 📘 t-SNE Docs: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html 📊 PCA Explained (Article): https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60 📊 Visualizing t-SNE: https://distill.pub/2016/misread-tsne/ --------------------------------------------------------------------------------------------------------------------------- 🧠 Watch the Full Data Science Series : 👉 Complete Playlist: https://youtube.com/playlist?list=PL07H6FsxVmmwoYzMdi2TEcb2Q6hQ0g-EV&si=Ed7T9olKUAr0I2h2 ------------------------------------------------------------------------------------------------------------------------- OUTLINE: 00:00:00 : Unlocking the Secrets of High-Dimensional Data 00:00:34 : Finding the Directions of Maximum Variance 00:01:49 : Eigenvectors and Projections 00:03:08 : Visualizing the Iris Dataset 00:04:40 : Mapping Neighbors to a Lower Dimension (t-SNE) 00:06:09 : Clustering the MNIST Digits (t-SNE Demo) 00:07:38 : Code in Action — Scikit-Learn for PCA and t-SNE 00:08:44 : PCA vs. t-SNE — Choosing the Right Tool 00:09:53 : Final Thoughts and Best Practices ------------------------------------------------------------------------------------------------------------------------------------------------------------- #datascience #dataanalytics #machinelearning #bigdata #deeplearning #artificialintelligence #ai #datavisualization #thedatakey #datasciencewiththedatakey #learnwiththedatakey #pca #tsne #fullcourse #datasciencecourse #datasciencefullcourse #education #foryou #subscribe #coding #newvideo #subscribemychannel #recommended #popular #python #pythontutorial #machinelearning #machinelearningfullcourse #relevant #chatgpt #dimensions #dimensionalityreduction