Summary
This video discusses the development of an ML-driven recruitment tool similar to Amazon’s rating system, which exhibited bias towards men and penalized certain keywords in resumes. The process of preparing data for machine learning involves planning, formulating problems, constructing models, and working with training sets. Data quality is emphasized, with a focus on collecting diverse datasets, cleaning data, and utilizing methods like sampling and imputation. Data formatting and normalization are crucial for consistency and standardization, while feature engineering helps create more efficient models.
ML-Driven Recruitment Tool
An experimental ML-driven recruitment tool similar to the Amazon rating system was designed to give job applicants scores ranging from one to five stars. However, it exhibited bias towards men and penalized resumes containing certain keywords.
Data Preparation for Machine Learning
The process of preparing data for machine learning involves planning, formulating the problem, constructing a training model, and processing the training set. Data quality is crucial, and a large, diverse dataset is essential for successful ML projects.
Data Cleaning and Reduction
Data cleaning and reduction involve collecting all possible data, analyzing it for errors, and removing irrelevant or duplicated information. Sampling methods are used to speed up the training process, and imputed data can be filled in for missing values.
Data Formatting and Normalization
Data formatting ensures consistency in data instances from multiple sources, while normalization standardizes features to have equal importance. Feature engineering involves creating new features to make the model more efficient.
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