Principal Component Analysis: Tutorial 13

In this tutorial of python for machine learning and data science; you will study about: 1. Introduction to Principle Component Analysis 2. Dimensionality Reduction: Feature Elimination and Feature Extraction 3. Standardization with example 4. Covariance Matrix with example Kindly do check out Labdhi Sheth github link: https://github.com/LabdhiSheth/Scikit-learn-tutorial Link to previous videos: 1. Preprocessing using Scikit Learn: Tutorial 2 (https://youtu.be/bHkiDjkXBXg) 2. Scikit-learn Supervised Learning Algorithms: Tutorial 3 (https://youtu.be/uObcUpFALpg) 3. Scikit-learn Supervised Learning Algorithms (Naive Bayes Theorem): Tutorial 4 (https://youtu.be/5bSzrmn1dEw) 4. Scikit-learn Supervised Learning Algorithms (K-Nearest Neighbors):Tutorial 5 (https://youtu.be/eCZrIWzI5B4) 5. Scikit-learn Supervised Learning Algorithms (Decision Tree Classifier): Tutorial 6 (https://youtu.be/aIxsbG4cdNI) 6. Hyper Parameter Tuning: Tutorial 7 (https://youtu.be/rFr5Z9pshVo) 7. Support Vector Machine: Tutorial 8 (https://youtu.be/2MebrP9_qQ4) 8. Logistic Regression: Tutorial 9 (https://youtu.be/u8o0U0lnkUA) 9. Supervised learning using Sklearn - Random Forest using Python: Tutorial 10 (https://youtu.be/ucr3Hz-cQ2Y) 10. Stochastic Gradient Descent: Tutorial 11(https://youtu.be/9e5H7wCA-WI) 11. K - Means Clustering: Tutorial 12 (https://youtu.be/LKT3z3cRDFA) #machine learning #principle component analysis #pca #python