Image Segmentation with Mean Shift Clustering

From image segmentation to anomaly detection, Mean Shift Clustering offers a versatile and powerful solution for a wide range of data analysis challenges. It is no ordinary algorithm - it's a dynamic and non-parametric technique that can navigate through complex data terrains, finding density peaks that lead to clusters of diverse shapes and sizes and more. In this guided project, you will learn how to identify complex patterns, clusters, and subgroups in your datasets and use it for image segmentation.

Building Recommender systems with Gaussian Mixture Model

Building Recommender systems, creating anomaly detection algorithm or performing customer segmentation are all very complicated but yet common tasks. Gaussian Mixture Model is a powerful probabilistic algorithm that can be a great tool to perform all of those tasks and more. In this guided project, you will learn how to identify complex patterns, clusters, and subgroups in your datasets by using GMMs.

Unraveling Patterns with DBSCAN

Classification and Clustering are one of the most common tasks in data science. If you ever need to classify a customer or perform anomaly detection, or even image segmentation, then DBSCAN is the right tool to use. In this project you will have a chance to learn what DBSCAN is and see how it's applied to some real world problems.

Video Processing - Subtracting Background with SVD

Want to know how to use Python to subtract background on a video easily? After doing this guided project, you will understand the foundation of singular-value decomposition and how to implement these techniques to edit frames in a video. As a bonus, you will also learn how to use SVD to reduce data dimensions with the scikit-learn as a professional data scientist.

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.

Use Kernel PCA To Find Why Are You Poor

Learn to identify patterns in data using Python programming and Data Science. Explore Kernel Principal Component Analysis by uncovering non linear trends, and draw valuable insights from your datasets. It's a powerful extension of traditional PCA that can unravel complex patterns and structures in non-linear data. Maping the data into a higher-dimensional feature space, where non-linear relationships become linear allows KPCA to capture the intricate structures and similarities in the data that may otherwise remain hidden.

Build an Image Retrieval System with NMF and More

How would you create an image retrieval system to find similar images? Non-Negative Matrix Factorization would be the right tool to use. It is an unsupervised learning technique used for decomposing data. Non-Negative Matrix Factorization will be a useful and powerful tool since factorized matrices can be interpreted as real images. Check out this guided project to find out what NMF is, how it works and how to apply it to solve real life business problems.