Data Science

Schumann Library guide to open access datasets for machine learning, computing, and data science projects.

A collection of eBooks and materials, a "learning path for becoming or improving as a data scientist with all books published in 2022. There is a focus on the foundational principles for MLOps, Maths and Python. Followed by a focus on Azure machine learning with caveats into ML on Kubernetes, Databricks and further MLOps." via O'Reilly Online Learning Platform.

A curated "expert playlist" of eBooks on O'Reilly Online Learning Platform. Aurélien Géron picks his favorites for machine learning, specifically focusing on TensorFlow and Scikit.

What You Will Learn
Find out why machine learning (ML) is called a black box
Get to grips with the features and labels of ML
Uncover techniques for training ML models
Find out the differences between supervised and unsupervised learning
Understand regression, clustering, and dimension reduction

This master class includes the following courses:
Linear Algebra for Machine Learning
Calculus for Machine Learning LiveLessons
Probability and Statistics for Machine Learning
Data Structures, Algorithms, and Machine Learning Optimization
Linear Algebra for Machine Learning LiveLessons provides you with an understanding of the theory and practice of linear algebra, with a focus on machine learning applications.