Explores five industry patterns for building robust content moderation and fraud detection systems using ML, including human-in-the-loop and data augmentation.
Explores active learning strategies for selecting the most valuable data to label when working with a limited labeling budget in machine learning.
A comprehensive list of 90 machine learning lecture videos covering topics from Python basics to advanced ML concepts like decision trees and Bayesian methods.
A comprehensive collection of 90 machine learning lecture videos covering Python, scikit-learn, algorithms, and model evaluation techniques.
A chronological survey of key NLP models and techniques for supervised learning, from early RNNs to modern transformers like BERT and T5.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and terminology from a university course.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and the shift from traditional programming.
Announcement for a lecture series on machine learning, covering topics like Weka, deep learning, algorithmic fairness, and sparse supervised learning.
An introduction to meta-learning, a machine learning approach where models learn to adapt quickly to new tasks with minimal data, like 'learning to learn'.
Explores Naive Bayes classifiers for text classification, covering theory and applications like spam filtering and song lyric analysis.
An introduction to Naive Bayes classifiers, focusing on their theory and application in text classification tasks like spam filtering.
An overview of predictive modeling, supervised machine learning, and the core workflow for pattern classification tasks.
An overview of predictive modeling, supervised machine learning, and pattern classification concepts, workflows, and applications.
