Offered By: IBMSkillsNetwork
Using PCA to Improve Facial Recognition
If an organization needs to process and identify individuals from a large database of images, each image may contain thousands of pixels, making it computationally expensive to compare and analyze directly. Applying PCA to these images, we can transform the pixel data into a reduced set of principal components. PCA empowers you to grasp the essence of each principal component and discover how they collectively capture the most important information present in your dataset. In this guided project, you will gain hands-on experience with PCA and learn how to apply it to solve real live problems.
Continue readingGuided Project
Data Science
173 EnrolledAt a Glance
If an organization needs to process and identify individuals from a large database of images, each image may contain thousands of pixels, making it computationally expensive to compare and analyze directly. Applying PCA to these images, we can transform the pixel data into a reduced set of principal components. PCA empowers you to grasp the essence of each principal component and discover how they collectively capture the most important information present in your dataset. In this guided project, you will gain hands-on experience with PCA and learn how to apply it to solve real live problems.
Throughout the project, you will be equipped with the tools to perform data compression, visualization, and denoising by leveraging the power of PCA. PCA is a methodology to reduce the dimensionality of a complex problem which you will be practicing in this guided project by using it on tasks like Facial Recognition, Image Compression and Finding patterns in data of high dimension in the field of quantitative finance.
By the end of this guided project, you will have mastered the art of Principal Component Analysis and its applications. You will be equipped to reveal hidden insights, compress data, and create impactful visualizations, making you a more proficient data explorer and analyst.Â