The document provides an introduction and background about the speaker, Kenichi Matsui. It discusses his career experience working for several large companies in software development, communications, and consulting. It then covers some of his current responsibilities related to data analysis and machine learning as a data scientist and group manager. Specific topics covered include an overview of data science skills and roles, machine learning techniques like classification and regression, and data analysis competitions.
The document provides an introduction and background about the speaker, Kenichi Matsui. It discusses his career experience working for several large companies in software development, communications, and consulting. It then covers some of his current responsibilities related to data analysis and machine learning as a data scientist and group manager. Specific topics covered include an overview of data science skills and roles, machine learning techniques like classification and regression, and data analysis competitions.
The document discusses text classification using Naive Bayes classifiers. It presents the Naive Bayes algorithm for classification, including calculating the probabilities of words given a class and class priors. It shows how to estimate these probabilities from training data and classify new documents. An example is provided to demonstrate classification of sample documents into positive and negative classes.