Skip to Main Content

Python in Digital Scholarship

This guide will provide an introduction to using Python in research and instruction and what resources are available in the Freedman Center.

Machine Learning with Python

Python has become the dominant language for machine learning (ML) and data science in both academia and industry. Its simplicity, coupled with powerful libraries, allows researchers and students to prototype algorithms quickly and work with large datasets for predictive modeling.

scikit-learn is the cornerstone library for classical machine learning in Python. Scikit-learn is a free, open-source library that provides a unified interface for a broad range of algorithms in supervised and unsupervised learning. It includes classification algorithms (e.g., logistic regression, support vector machines, decision trees, random forests), regression algorithms, clustering (e.g., k-means, DBSCAN), dimensionality reduction (PCA), and more, all optimized and easy to use. Scikit-learn is built on NumPy and SciPy, meaning it works efficiently with numerical arrays and integrates well with the scientific Python stack. Its API is designed for consistency – for instance, every model has .fit(X, y) to train and .predict(X) to make predictions, making it straightforward to learn and switch between algorithms.

A simple example using scikit-learn:

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)        # X_train and y_train are training data and labels
predictions = model.predict(X_test)
print("Predictions:", predictions)
print("Coefficients:", model.coef_)
print("Intercept:", model.intercept_)

This code fits a linear regression model and then makes predictions on new data. Scikit-learn also provides utilities for model evaluation (metrics for accuracy, precision, recall, etc.), model selection (train/test splitting, cross-validation, grid search for hyperparameter tuning), and data preprocessing (scaling features, encoding categorical variables).

For deep learning and more complex models, Python offers libraries like TensorFlow and PyTorch. These frameworks support building and training neural networks, which are used in cutting-edge research for tasks such as image recognition, speech processing, and advanced NLP. Both TensorFlow (originated by Google) and PyTorch (by Facebook/Meta) are open-source and widely used in research for developing custom neural network architectures (e.g., CNNs, RNNs, Transformers). They provide automatic differentiation (for gradient computation), GPU acceleration, and high-level APIs. Keras (now integrated with TensorFlow) offers a user-friendly interface for building neural networks with minimal code, which is helpful for beginners or rapid prototyping.

Python’s ecosystem in ML is complemented by Pandas and NumPy for data handling, and by Matplotlib/Seaborn for visualizing model results or data. Workflows often involve using Pandas to prepare and clean data, scikit-learn or TensorFlow/PyTorch to build models, and then libraries like Matplotlib to plot learning curves or results.

It is also noteworthy that Python’s rise in machine learning is supported by a huge community and resources: there are abundant tutorials, sample code (on sites like Kaggle), and active development of new algorithms. Many research papers release Python code or libraries for their methods, making cutting-edge techniques accessible. As a result, students and researchers can implement complex models relatively easily and focus on experimentation and interpretation.