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Latent Semantic Analysis for NLP eBook for free
Latent Semantic Analysis for NLP eBook for free
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- five seats for your team
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Latent Semantic Analysis for NLP eBook for free
Look inside
At Finative, an ESG analytics company, you’re a data scientist who helps measure the sustainability of publicly traded companies by analyzing environmental, social, and governance (ESG) factors so Finative can report back to its clients. Recently, the CEO has decided that Finative should increase its own sustainability. You’ve been assigned the task of saving digital storage space by storing only relevant data. You’ll test different methods—including keyword retrieval with TD-IDF, computing cosine similarity, and latent semantic analysis—to find relevant keywords in documents and determine whether the documents should be discarded or saved for use in training your ML models.
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.
prerequisites
This liveProject is for ML engineers, intermediate-level Python programmers, and early-stage data scientists who are familiar with the basics of linear algebra. 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 systems of linear equations, vector spaces, and matrix transformations
- Basic familiarity with NumPy (indexing arrays, array creation, and manipulation)
- Basic understanding of regular expressions to manipulate a string
- Basic linear algebra
- Basic data science
you will learn
In this liveProject, you’ll learn how to preprocess text data using NLP tools, including regular expressions, tokenization, and stop-word removal.
- Mathematical insights into singular value decomposition (SVD) and why it is such a powerful and useful algorithm
- Basic mathematics of cosine similarity and when to apply it
- How to tokenize, clean, and prepare text data
- The algorithm and mathematical principles of Term Frequency - Inverse Document Frequency (TF-IDF)
- The mathematical concepts and application of latent semantic analysis (LSA), singular value decomposition (SVD), how it differs from cosine similarity, and when to apply it
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- 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.
