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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.
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
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.
geekle is based on a wordle clone.