PyTorch: A Quick & Dirty Intro
This article provides a hands-on introduction to PyTorch, covering installation, building a simple linear regression model, data preparation, training, evaluation, and further resources.
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Docker Crash Course for Data Scientists
This Docker crash course for data scientists covers Docker fundamentals like architecture, images, containers, storage, networking. It then explores using Docker for data science workflows including environments, model training/deployment, notebooks. Finally it discusses best practices for optimization, orchestration, security, and monitoring.
Handling Categorical Variables in scikit-learn: Strategies and Encoding Techniques
Categorical variables must be encoded before use in scikit-learn models. This article covers 3 of the core strategies and best practices for handling categorical features in machine learning with code examples.
An Overview of Feature Selection Techniques in scikit-learn
This article provides an overview of the main feature selection techniques available in scikit-learn.
Evaluating Classification Model Performance in scikit-learn
This article provides an overview of how to evaluate classification model performance in scikit-learn using metrics like accuracy, precision, recall, F1 score, and ROC AUC. It includes code examples and explanations of each metric.