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Data Science Internship Prep
·
Module 8
End-to-End Machine Learning Portfolio Project
1
Problem Definition, Data, and Metrics
Define a machine learning problem and select an appropriate dataset and evaluation metric.
2
Feature Engineering and Preprocessing Pipelines
Build a complete feature engineering and preprocessing pipeline for the chosen dataset.
3
Cross-Validation for Model Training and Evaluation
Train, tune, and evaluate multiple candidate models using cross-validation.
4
Interpreting Model Predictions with SHAP and Error Analysis
Analyze the final model's predictions and behavior using SHAP and error analysis.
5
Dockerizing a FastAPI Application
Containerize the FastAPI application for the model using Docker.
6
Documenting Your Project Workflow in Jupyter
Write a comprehensive Jupyter Notebook that documents the entire project workflow and justifies key decisions.
7
Publishing Your Project to GitHub
Publish the complete project to GitHub, including the notebook, API code, and a professional README.
Previous module
Model Explainability and Deployment
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Applied NLP: Embeddings and Vector Databases