Math for Machine Learning

Principal Component Analysis you own this product

This free project is part of the liveProject series Math for Machine Learning
prerequisites
intermediate Python (particularly NumPy) • basics of linear algebra (particularly systems of linear equations and matrices)
skills learned
algorithm optimization with principal component analysis (PCA) • matrix manipulation with NumPy • nuances of scikit-learn's PCA library
Nicole Königstein
1 week · 6-8 hours per week · INTERMEDIATE

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Step into the role of data scientist at Finative, an analytics company that uses environmental, social, and governance (ESG) factors to measure companies’ sustainability, a brand new, eco-focused trend that's changing the way businesses think about investing. To provide its clients with the valuable insights they need in order to develop their investment strategies, Finative analyzes a high volume of data using advanced natural language processing (NLP) techniques.

Recently, your CEO has decided that Finative should increase its own sustainability. Your task is to develop a method to optimize the runtime for the company’s machine learning models. You’ll apply principal component analysis (PCA) to the data in order to speed up the ML models. To classify handwritten digits and prove your theory that PCA speeds up ML algorithms, you’ll implement logistic regression with scikit-learn. You’ll use the explained variance ratio to gain an understanding of the trade-offs between speed and accuracy. When you’re done, you’ll be able to present your CEO with proof of PCA’s efficiency in optimizing runtime.

project author

Nicole Königstein

Nicole Königstein currently works as data science and technology lead at impactvise, an ESG analytics company, and as a quantitative researcher and technology lead at Quantmate, an innovative FinTech startup that leverages alternative data as part of its predictive modeling strategy. She’s a regular speaker, sharing her expertise at conferences such as ODSC Europe. In addition, she teaches Python, machine learning, and deep learning, and holds workshops at conferences including the Women in Tech Global Conference.

prerequisites

This liveProject is for ML engineers, intermediate-level Python programmers, and early-stage data scientists who want to gain an understanding of the mathematical foundations of PCA and how they can use this simple, yet powerful, algorithm in their own projects. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Intermediate Python (declaring variables, loops, branches, working with arrays)
  • How to use Jupyter Notebook
  • Understanding of vectors and matrices
  • Basic familiarity with NumPy (indexing arrays, array creation, and manipulation)
  • Basic familiarity with scikit-learn (how to import and use classes such as sklearn.decomposition)
TECHNIQUES
  • Basic linear algebra
  • Basic statistics
  • Basic data science

you will learn

In this liveProject, you’ll learn to improve the runtime of ML models by using principal component analysis (PCA) to reduce the dimensionality of your data.

  • Fundamental linear algebra techniques used to compute PCA
  • Use NumPy to transition your newly gained mathematical knowledge into code
  • Apply scikit-learn’s PCA library and learn about its nuances
  • Understand the benefits of dimensionality reduction and the trade-offs between speed and accuracy

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