Machine Learning - Dimensionality Reduction - Feature Extraction & Selection

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Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.

This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.

This #MachineLearning with #Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!

Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.

Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.

Get ready to do more learning than your machine!

Connect with Big Data University:

ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.

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I am working on project plant leaf disease detection using SVM can I use pca in it for feature extraction?

rajeshadam
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it is a nice video. very helpful for me ....thank you

sanoopksanu
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thanks nice video and easy to understand

Waterlmelon
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that is very nice and simple video. Thank you

RushSina
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The "algebraic" explanation is very bad. In algebra, "ab" means the product of a and b, so involves two terms and is no more tractable than a + b. What you really meant is something like substituting x = a + b. But it's still a pretty bad analogy.

gorgolyt
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Hey! I'm starting and it helped a lot. Just letting you know. Thanks!

azmainyakinsrizon