Description: It is for estimating and testing statistical models. Supports statistical models, hypothesis testing, and regression analysis.
Use in ML: Statistical analysis and hypothesis testing.
Description: Optimizing machine learning pipelines, including data preparation, model selection, and model hyperparameters.
Use in ML: Automates the search for optimal hyperparameters.
Description: Set of tools to provide lightweight pipelining in Python. Useful for parallelizing and caching computations.
Use in ML: For saving and loading large datasets in parallel.
Description: NLP library built on PyTorch. Focuses on high-quality research and provides pre-built models for common tasks.
Use in ML: Useful for building and training NLP models.
Our effort in this collection is to keep the audience interested in the fields of data science and artificial intelligence as well as the Python programming language updated. We are honored to share your questions with us so that we can exchange useful knowledge.
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It is important to structure your data science project based on a certain standard so that your tea…