5, 10 or 20 seats+ for your team - learn more
Put on your data scientist hat for this series of liveProjects, where you’ll work 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. In each liveProject, you’ll focus on different machine learning (ML) and deep learning (DL) mathematical approaches—including Bayes' theorem, principal component analysis (PCA), cosine similarity, latent semantic analysis, and backpropagation—as you help Finative accomplish its goal of increasing its own sustainability.
You’ll develop a method to reduce the runtime of ML models, and you’ll save digital storage space by finding relevant keywords in order to determine whether documents should be discarded or saved. To increase efficiency, you’ll save training time by using a pre-trained language model to classify a sustainability report. Then, you’ll analyze the sentiment of tweets in order to detect greenwashing, the practice of spreading disinformation about a company’s sustainability. When you’re finished with these liveProjects, you’ll have a solid understanding of the mathematical basics of machine learning, strong programming and data science skills, and familiarity with sustainability.
The takeaway from the author is very insightful. In general, I would say this project is very helpful and we can learn the theory and practice at the same time.
The series walks you through the basic math employed in different aspects of ML.
These liveProjects are for ML engineers, intermediate-level Python programmers, and early-stage data scientists. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In these liveProjects, you will learn progressively more complex mathematical techniques that will deepen your understanding of ML models and strengthen the skills you need for long-term success as a data scientist or machine learning engineer.
geekle is based on a wordle clone.