In this Open Lab, Digital Humanities Analyst Namig Abbasov, will begin with an intuitive exploration of unsupervised machine learning.
Following that, we will review clustering and dimensionality reduction and techniques for these tasks including k-means clustering and principal component analysis.
The Data Science and Analytics Unit at the ASU Library is excited to announce the return of our Volunteer Open Projects. We have four Spring 2024 Open Projects to choose from this semester. The Volunteer Open Projects are an opportunity to get involved with real-world issues, to collaborate, learn, and expand your knowledge and expertise in data science and analytics, regardless of your discipline or level of experience.
Join the ASU Library for Archives Wednesday, an open house inside Hayden Library’s Wurzburger Reading Room.
This Open Lab will introduce and explore key concepts in Artificial Intelligence. The aim is to understand applications, but also think through implications for your own research and professional interests.
In this Open Lab, Digital Humanities Analyst Namig Abbasov, will explore how AI tools reshape social science and digital humanities research, not only opening new research avenues but also brining new challenges.
In this Open Lab, Digital Humanities Analyst Namig Abbasov will utilize Python to navigate the key stages of quantitative data analysis in social science research. First, we will preprocess data, recoding and transforming necessary variables to ensure that the dataset is ready for statistical analysis.
In the initial segment of this Open Lab, Digital Humanities Analyst, Namig Abbasov, will examine text analysis using unsupervised methods, focusing on topic modeling techniques. In the latter segment, we will investigate word embeddings, an increasingly popular approach for feature extraction in text analysis.
In this Open Lab, Digital Humanities Analyst,Namig Abbasov, will first quickly review the fundamentals of text analysis and then apply a range of supervised learning techniques, including linear and logistic regression, L1 and L2 regularization, cross-validation (CV), K-Nearest Neighbors (KNN) and Support Vector Machines (SVMs), to analyze text data.
There is more to learn! Building on the exploration of Unsupervised Machine Learning in the prior week, in this Open Lab, Digital Humanities Analyst Namig Abbasov, will cover association rule learning and reinforcement techniques and their applications. Time permitting, we will also conceptually examine semi-supervised learning.
Expanding upon the principles of Supervised Machine Learning, in this Open Lab, Digital Humanities Analyst Namig Abbasov, will cover machine learning techniques tailored for regression problems, along with the metrics used to evaluate these techniques. We will also examine ensemble learning methods as much as time allows.