Go Zero-One by learning the much-needed fundamentals of “Dimensionality Reduction Techniques". In this course, you will learn " Linear Principal Component Analysis, Kernel Principal Component Analysis, Automatic Feature Selection".
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Go Zero-One by learning the much-needed fundamentals of “Dimensionality Reduction Techniques". In this course, you will learn " Linear Principal Component Analysis, Kernel Principal Component Analysis, Automatic Feature Selection".
Features of a dataset are an integral part of the model building process but having too many features at the same time can be a big challenge in improvising a model’s performance. Don’t want your machine learning model to suffer from ‘The curse of dimensionality’? We are here with a solution for you. With an amazing crash course on “Dimensionality Reduction” wherein you’ll get to know how to tackle dimensionality related problems, what Principle Component Analysis (PCA) is and how it helps, feature reduction, Thresholding Numerical Feature Variance, Handling Highly Correlated Features and why and much more. You will be able to code as you learn.
Upon successful completion of the Course, You will receive a Verifiable certificate with QR code. Now employers can verify the certificates just by scanning QR code or by verification ID.
The knowledge and skills you've gained working on projects, simulations, case studies will set you ahead of competition.
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