Reading
The following material is optional. However, interested readers are encouraged to peruse it.
Machine Learning, Explained
Sara Brown
April 21, 2021
This readable, non-technical article, digitally penned by writer Sara Brown for the Ideas Made to Matter website of the MIT Sloan School of Management, offers a comprehensive view of machine learning (ML), including a discussion of the three main categories of ML: supervised, unsupervised, and reinforcement. Brown also discusses some of the common ML technologies and algorithms, including artificial neural networks and deep learning. Although written from a business perspective and including many examples of business applications, the article also explains many ML approaches that are directly relevant to the digital humanities, including natural language processing and image analysis. Brown also emphasizes the importance of data in machine learning.
READ: Machine Learning, Explained
Machine Learning. An Introduction
Gavin Edwards
November 18, 2018
This longer and more detailed introduction to machine learning discusses the topic in an intuitive, relatively non-technical manner. It also presents background theory and clarifies terminology. Concepts that are important in the context of the digital humanities are also examined, including classification, clustering, regression, anomaly detection, and dimensionality reduction.
READ: Machine Learning. An Introduction
Which Machine Learning Algorithm Should I Use?
Hui Li
December 9, 2020
The SAS Data Science Blog
This blog site lists the main techniques in machine learning and discusses the different categories of machine learning algorithms. The site provides a resource for data scientists and others in selecting appropriate machine learning algorithms to solve specific problems. It features a “Machine Learning Algorithms Cheat Sheet” graphic to aid in the selection process.
VIEW: Which Machine Learning Algorithm Should I Use?
Parameters and Hyperparameters in Machine Learning and Deep Learning
Kizito Nyuytiymbiy
Dec 30, 2020
This article explains the difference between parameters and hyperparameters and the role of both in machine learning.
Read: Parameters and Hyperparameters in Machine Learning and Deep Learning
Python and R Code
The regression example presented in this section is based on the Python code: Regression_Example.py. Because the data are generated randomly, the exact coefficient values may differ slightly. A Jupyter Notebook (Regression_Example.ipynb) is available for this code. The R code for the interactive visualization (GenderedPerspectives_Visualization.R) is available. It uses the data file genderedPerspectives_data4r.csv. A Jupyter Notebook for this visualization (GenderedPerspectives_Visualization.ipynb) is also available.