Data Processing Tools with NumPy

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prerequisites
basic Python (Jupyter Notebook, NumPy, RegEx) • basic matrix operations • basic knowledge of trie data structure • basic knowledge of TF-IDF and SVD
skills learned
extract features with NumPy • vectorize text with TF-IDF and SVD • compute similar items with embedded vectors
Ruihao Qiu
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside

As a data engineer in an online recruiting tech company or a large organization’s HR department, you’ll build a series of practical tools to process and extract useful information from unstructured text data using NumPy. You’ll learn important methods (including trie data structure, TF-IDF, SVD), how to implement them, and their applications in the real world. When you’re finished, you’ll have the know-how to build data processing tools that meet the needs of machine learning engineers, data analysts, and product developers.

This project is a part of the series DS Pipeline with Python.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

This liveProject is for Python beginners who are interested in building data processing tools using NumPy. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Basic Python
  • Basic Jupyter Notebook/JupyterLab
  • Basic NumPy and pandas
TECHNIQUES
  • Basic matrix operations
  • Basic knowledge of tree data structure
  • Basic concept of TF-IDF, SVD (what they’re named for and used for)
  • Basic understanding of tokenization and cleaning of text data

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