PCA vs t-SNE in Data Science: Understanding Dimensionality Reduction by Sudhir Kumar Singh.

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Welcome to our channel! In this video, we dive deep into the world of dimensionality reduction in data science, focusing on two popular techniques: Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE).
What You'll Learn:
Introduction to Dimensionality Reduction: Understand the importance of reducing the number of variables in your dataset while preserving its essential patterns.
PCA Explained: Learn how PCA transforms your data into a new set of uncorrelated variables (principal components) and helps in capturing the maximum variance in your dataset.
t-SNE Unveiled: Discover how t-SNE excels in visualizing high-dimensional data by converting similarities between data points into probabilities, creating a lower-dimensional representation that reveals clusters and patterns.
Key Differences: Explore the fundamental differences between PCA and t-SNE, including their underlying algorithms, use cases, advantages, and limitations.
Practical Applications: See real-world examples and use cases where PCA and t-SNE shine, helping you decide which technique to use for your specific data science projects.
Hands-On Demo: Follow along with a step-by-step demonstration of how to implement PCA and t-SNE using Python, featuring popular libraries like scikit-learn.
Whether you're a data science beginner or a seasoned professional, this video will provide you with valuable insights into PCA and t-SNE, enhancing your ability to make sense of complex datasets.
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[@ArunKumar_DE]

Good explanation Sudhir Sir