PCA Explained Step by Step Numerical Example for Beginners & Pros 🧮📊 скачать в хорошем качестве

PCA Explained Step by Step Numerical Example for Beginners & Pros 🧮📊

"Welcome to this step-by-step tutorial on Principal Component Analysis (PCA)! Whether you’re a beginner just starting out in data science or a professional looking to deepen your understanding, this video is for you. In this tutorial, we’ll walk through a numerical example of PCA from scratch, breaking down each step in a clear and easy-to-follow way. You'll learn: What PCA is and why it’s so powerful for dimensionality reduction. How to calculate covariance matrices and eigenvectors step by step. How to transform your data into principal components. Practical insights into interpreting the results. 🔗 Resources: GitHub Repository: [(https://github.com/DeepKnowledge1/ml)] Playlist: [(   • Machine Learning : From Basics to Advanced  )] By the end of this video, you'll have a solid understanding of PCA and be able to apply it to your own datasets. We’ll also touch on how PCA can improve machine learning models and simplify data visualization. 💡 Why Watch This Video? Perfect for beginners with no prior knowledge of PCA. Detailed numerical examples to build intuition without heavy math jargon. Tips for professionals to optimize PCA in real-world projects. Bonus Python code walkthrough for practical implementation. 📚 Resources Mentioned in the Video: [Link to Dataset Used] [Link to Python Code (Google Colab/Repository)] [Recommended Books/Papers on PCA] 📢 Subscribe to the Channel! If you found this tutorial helpful, don’t forget to like, subscribe, and hit the notification bell so you never miss an upload. Let me know in the comments what other machine learning topics you’d like me to cover! --- #pca #principalcomponentanalysis #datascience #machinelearning #dimensionalityreduction #eigenvectors #covariancematrix #datavisualization #beginnerstutorial #stepbystep #python #mltutorial #dataanalysis #unsupervisedlearning #eigenvalues #datascientist #learndata