courseraПрограммирование

Machine Learning Fundamentals

Курс от Dartmouth College

Средний≈ 34.1 чАнглийский

О курсе

This course provides a brief introduction to the theory and practice of supervised machine learning, the discipline of teaching computers to make predictions from labeled data. We begin with a well-known model of linear regression, moving from fundamental principles to the advanced regularization techniques essential for building robust models. We then transition from regression to classification, exploring two major paradigms for separating data: discriminative models and generative models. The course concludes in learning how to critically evaluate and compare classifier performance using industry-standard tools such as the ROC Curve. Upon completion, you will have a strong command of the core principles that underpin modern predictive modeling.

Навыки, которые вы освоите

Regression Analysis Classification Algorithms Machine Learning Logistic Regression Model Evaluation Machine Learning Algorithms Probability & Statistics Statistical Modeling Predictive Modeling Machine Learning Methods Supervised Learning Statistical Machine Learning Model Optimization Applied Machine Learning Generative Model Architectures

Программа курса

7 модулей · 68 учебных материалов

01Course Overview2 материалов

Course Orientation

Course OverviewЧтение Introduction to Machine LearningВидео

02Foundations and Basic Linear Regression12 материалов

Module Overview: Foundations and Basic Linear RegressionЧтение

Учитесь у экспертов

Peter Chin

Professor of Engineering