Data Augmentation for ML you own this product

prerequisites
basic Python (Jupyter Notebook, SciPy, NLTK) • basic data augmentation • basic statistical modeling • basic object-oriented programming (OOP)
skills learned
data augmentation for ML • deal with missing data with statistical modeling • build data augmentation tools with OOP
Ruihao Qiu
1 week · 6-8 hours per week · INTERMEDIATE
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As a machine learning engineer in an online recruiting tech company or HR department of a large organization, your task is to address a lack of data, a common problem in data science projects. To solve this, you’ll create multiple tools to augment processed data, increasing its volume and learning essentials about probability distributions, random sampling, and OOP. Completing this project will enhance your data analysis and visualization skills, taking you further down the path to a career in data science.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Ruihao Qiu

Ruihao Qiu is the senior data scientist at a German tech company and has more than five years experience in data science and machine learning. As part of the process of earning his PhD in statistical physics, he developed statistical models to simulate and search for new nanomaterials. In his early days as a data science consultant, he helped his clients from DAX30 multinational companies solve real-world data challenges. As a senior data scientist, he designed and built data pipeline and ML recommender systems for online recruitment applications. He enjoys taking on different career roles and sharing ideas about his data science work in tech blogs and in public presentations.

prerequisites

This liveProject is for Python beginners who want to learn how to improve their data analysis skills with data augmentation. To begin these liveProjects you’ll need to be familiar with the following:

TOOLS
  • Basic Python
  • Basic Jupyter Notebook/JupyterLab
  • Basic SciPy
  • Intermediate pandas
  • Basic plotting with Matplotlib
  • Basics of NLTK (Natural Language Toolkit)
TECHNIQUES
  • Basic statistics
  • Basics of plot types
  • Basics of object-oriented programming (OOP)

features

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book resources
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