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Unsupervised Machine Learning

Learn Unsupervised Machine Learning Hands-On

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7 Guided Projects

About this Learning Path

This Learning Path introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning. The hands-on section of this Learning Path focuses on using best practices for unsupervised learning.

 

Who should take this Learning Path?
This Learning Path targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.

 

What skills should you have?

To make the most out of this Learning Path, you should have familiarity with programming on a Python development environment, as well as a fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Average Course Rating

4.6 out of 5

Skills You Will Learn

Python, Data Science, Clustering, Machine Learning, Computer Vision, Data Visualization, Image Processing, Video, Python Programming, General Statistics


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  • Image Segmentation with Mean Shift Clustering
    Beginner Guided Project Data Science

    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.

    4.5
    (120 Reviews)
    965 Enrolled
    30 Minutes
  • Building Recommender systems with Gaussian Mixture Model
    Beginner Guided Project Data Science

    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.

    4.7
    (46 Reviews)
    234 Enrolled
    30 Minutes
  • Unraveling Patterns with DBSCAN
    Beginner Guided Project Data Science

    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.

    4.7
    (37 Reviews)
    177 Enrolled
    30 Minutes
  • Video Processing - Subtracting Background with SVD
    Intermediate Guided Project Machine Learning

    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.

    4.6
    (74 Reviews)
    361 Enrolled
    45 Minutes
  • Using PCA to Improve Facial Recognition
    Beginner Guided Project Data Science

    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.

    4.7
    (35 Reviews)
    192 Enrolled
    30 Minutes

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