Probability
Explore Bayes Theorem, Hidden Markov Models, Shannon Entropy, Naive Bayes classifier, Beta distribution, and Thompson sampling in this friendly introduction to key probability concepts.
Machine Learning
Comprehensive introduction to key machine learning concepts, algorithms, and applications, covering testing, error metrics, recommendation systems, and various classification methods.
Machine Learning Fun and Easy
Explore popular machine learning concepts through engaging lectures covering regression, clustering, neural networks, and more. Gain fundamental knowledge in an accessible, enjoyable format.
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Explore data augmentation MCMC for Bayesian inference from privatized data, focusing on differential privacy challenges and solutions in statistical analysis.
Supervised Machine Learning Tutorial - Logistic Regression - Naive Bayes Classifier
Explore logistic regression and Naive Bayes classifiers for supervised machine learning. Learn their advantages, applications, and implementation in classification tasks for data-driven decision making.
Is That Spam in My Ham?
Learn to build a basic spam classifier using Python and Naive Bayes. Explore key machine learning concepts, implement a model to detect spam emails, and understand how to measure and improve classification performance.
Recipe for Text Analysis in Social Media
Explore text analysis in social media using Naïve Bayes classifiers, sentiment analysis, and linguistic information. Learn about opinion lexicons and supervised vs unsupervised learning approaches.
Building a Naive Bayes Text Classifier with scikit-learn
Learn to build a Naive Bayes text classifier using scikit-learn, exploring preprocessing techniques and feature extraction methods for effective spam detection in YouTube comments.
Machine Learning: Bayesian Learning and Naive Classifiers - Lecture 27
Master Bayesian learning concepts through practical examples like tennis prediction, exploring prior probability, model definitions, and naive classifiers for binary classification problems.