PCA Simplified for Beginners | Principal Component Analysis Explained in Under 15 Min | Scaler
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Here is a detailed tutorial
Here is a detailed tutorial for beginners on Principal Component Analysis by Harshit Tyagi. (Instructor, Machine Learning) In this video, you will learn all the important concepts with regard to PCA. Learn more about Scaler now: https://bit.ly/3sdMLPv
What is Principal Component Analysis (PCA)?
Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
Reducing the number of variables of a data set naturally comes at the expense of accuracy, but the trick in dimensionality reduction is to trade a little accuracy for simplicity. Because smaller data sets are easier to explore and visualize and make analyzing data much easier and faster for machine learning algorithms without extraneous variables to process.
Why is PCA used?
PCA forms the basis of multivariate data analysis based on projection methods. The most important use of PCA is to represent a multivariate data table as a smaller set of variables (summary indices) in order to observe trends, jumps, clusters and outliers. This overview may uncover the relationships between observations and variables, and among the variables.
The following topics are covered in this PCA tutorial:
0:00 - Introduction
1:15 - Principal component analysis
2:48 - How PCA works
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What is Principal Component Analysis (PCA)?
Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
Reducing the number of variables of a data set naturally comes at the expense of accuracy, but the trick in dimensionality reduction is to trade a little accuracy for simplicity. Because smaller data sets are easier to explore and visualize and make analyzing data much easier and faster for machine learning algorithms without extraneous variables to process.
Why is PCA used?
PCA forms the basis of multivariate data analysis based on projection methods. The most important use of PCA is to represent a multivariate data table as a smaller set of variables (summary indices) in order to observe trends, jumps, clusters and outliers. This overview may uncover the relationships between observations and variables, and among the variables.
The following topics are covered in this PCA tutorial:
0:00 - Introduction
1:15 - Principal component analysis
2:48 - How PCA works
We are a tech-focused upskilling and reskilling platform catering to tech enthusiasts in universities and working professionals. There are more Scaler graduates working at Amazon than all of the IITs combined!
Learn more about Scaler: https://bit.ly/3sdMLPv
📌 Follow us on Social and be a part of an amazing tech community📌
👉 Meet like-minded coder folks on Discord - Discord: discord
👉 Tweets you cannot afford to miss out on - Twitter: scaler_official
👉 Check out student success stories, expert opinions, and live classes on LinkedIn - LinkedIn: scalerofficial
👉 Explore value packed reels, carousels and get access to exclusive updates on Instagram - Instagram: scaler_official
📢 Be a part of our one of a kind telegram community: https://t.me/Scalercommunity
🔔 Hit that bell icon to get notified of all our new videos 🔔
If you liked this video, please don't forget to like and comment. Never miss out on our exclusive videos to help boost your coding career! Subscribe to Scaler now!
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#pca #principalcomponentanalysis #scaler #createimpact
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