Dimensionality Reduction with PCA, t-SNE and UMAP

This project is part of the Hands-on Data Science with Julia bundle.
prerequisites
basics of Julia • intermediate familiarity with scikit-learn and dimensionality reduction algorithms
skills learned
PCA, t-SNE, and UMAP dimensionality reduction techniques • validating and analyzing output of PCA algorithm • calling Python modules from Julia
Łukasz Kraiński and Bogumił Kamiński
1 week · 4-6 hours per week · INTERMEDIATE
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liveProject This project is part of the Hands-on Data Science with Julia bundle. liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save: $10 (33%)
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In this liveProject, you’ll use the Julia programming language and dimensionality reduction techniques to visualize housing sales data on a scatter plot. This visualization will allow the marketing team to identify links and demand patterns in sales, and is also a useful tool for noise reduction or variance analysis. You’ll use the popular PCA algorithm to visualize the sales dataset with overlaid clustering assignments from k-means and DBSCAN methods, and then expand Julia’s capabilities by calling Python modules using the PyCall.jl package. This extra flexibility will allow you to explore the t-SNE and UMAP algorithms which have excellent results for high-dimensional datasets.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project authors

Bogumil Kaminski
Bogumił Kamiński is a Head of the Decision Analysis and Support Unit and Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He also holds a position of Adjunct Professor at the Data Science Laboratory on Ryerson University and is affiliated with Fields Institute (Computational Methods in Industrial Mathematics Laboratory). In Julia community, he is owner of JuliaData organization and member of JuliaStats and JuliaLang organizations on GitHub. He also contribute to the community as top answerer for [julia] tag on StackOverflow.
Lukasz Krainski
Lukasz Krainski is a Research Assistant at the Decision Analysis and Support Unit at SGH Warsaw School of Economics. He is certified cloud engineer with expertise in Azure and GCP cloud platforms. You can find him at tech conferences speaking about MLOps and AI (MLinPL 2019, PositivTech 2020, Data Driven Innovation 2020). Lukasz is also active developer and maintainer of Julia packages (CGE.jl, SmartTransitionSim.jl).

prerequisites

This liveProject is for experienced data scientists and data analysts who are interested in building their skills in Julia. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of Jupyter Notebook
  • Basics of Julia and intermediate experience in another high-level programming language such as Python or R
  • Intermediate knowledge of scikit-learn and umap-learn Python packages
  • Intermediate knowledge of plotting libraries
  • Basic usage of MultivariateStats.jl package
  • Basics of PyCall.jl and Conda.jl packages
  • Basics of Arrow data format
TECHNIQUES
  • Intermediate usage of dimensionality reduction algorithms
  • Intermediate analysis of PCA dimensionality reduction characteristics
  • Basic scatterplots in 2D and 3D
  • Basics of calling Python from Julia

you will learn

In this liveProject, you’ll master dimensionality reduction, unsupervised learning algorithms, and put the powerful Julia programming language into practice for real-world data science tasks.

  • PCA, t-SNE, and UMAP dimensionality reduction techniques
  • Validating and analyzing output of PCA algorithm
  • Calling Python modules from Julia
  • Combining clustering and dimensionality reduction results

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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