Principal Component Analysis (PCA) | Dimensionality Reduction in Machine Learning | Tutort Academy

Principal Component Analysis (PCA) Explained | Dimensionality Reduction in Machine Learning | Tutort Academy Learn the fundamentals of Principal Component Analysis (PCA) and understand how this powerful dimensionality reduction technique simplifies complex datasets while preserving the most important information in Machine Learning, Data Science, and Artificial Intelligence. In this session, you'll build a strong foundation by exploring how PCA works, principal components, variance, covariance matrix, eigenvalues, eigenvectors, feature transformation, dimensionality reduction, explained variance, advantages, limitations, and real-world applications. 🎯 Perfect for beginners, data science aspirants, and machine learning enthusiasts. 🔹 About Tutort Academy: Tutort Academy is a career-focused EdTech platform based in Bengaluru, empowering professionals with industry-ready skills in Data Structures & Algorithms, System Design, Machine Learning, Data Science, and Generative AI. Our programs combine expert mentorship, hands-on learning, and dedicated placement support to help learners accelerate their careers in tech. Our learners have successfully secured roles at top companies like Google, Amazon, Microsoft, Intuit, and Walmart. 🔗 Follow us: 🌐 Website: https://www.tutort.net/ 💼 LinkedIn:   / tutort   📸 Instagram:   / tutort.academy   🐦 Twitter:   / tutort_academy   📢 Support Us: If this helps you, don't forget to like, share, and subscribe 🔔 #PCA #MachineLearning #DataScience #ArtificialIntelligence #TutortAcademy