courseraЗдоровье

Machine Learning in Healthcare: Fundamentals & Applications

Курс от Northeastern University

Начальный≈ 18.4 чАнглийский

О курсе

Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.

Навыки, которые вы освоите

Artificial Intelligence Artificial Neural Networks Unsupervised Learning Model Evaluation Data Mining Machine Learning Model Training Machine Learning Software Healthcare Industry Knowledge Machine Learning Methods Deep Learning Responsible AI Machine Learning Algorithms Algorithms Applied Machine Learning Random Forest Algorithm Diagnostic Tests Predictive Modeling Health Technology

Программа курса

4 модулей · 85 учебных материалов

01Demystifying Data Mining and Artificial Intelligence23 материалов

Getting Started

Welcome to Machine Learning in Healthcare: Fundamentals & ApplicationsЧтение SyllabusЧтение Meet Your Faculty: Paul CerratoВидео Meet Your Faculty: Sonya MakhniВидео

Учитесь у экспертов

Sonya Makhni

Преподаватель курса

Paul Cerrato

Visiting Lecturer

Machine Learning in Healthcare: Fundamentals & Applications

О курсе

Навыки, которые вы освоите

Программа курса

Getting Started

Учитесь у экспертов

Sonya Makhni

Paul Cerrato

Module 1 Overview

Lesson 1: Defining Data Mining

Lesson 2: Differences Between Machine Learning and Deep Learning

Lesson 3: Linear Regression

Lesson 4: Datasets

Module Wrap-Up

Module 2 Overview

Lesson 1: Logistic Regression

Lesson 2: Decision Trees and Random Forest Modeling

Lesson 3: Gradient Boosting

Lesson 4: Clustering

Lesson 5: Neural Networks

Module Wrap-Up

Honors Lesson: Dive Deeper Into Algorithm Models

Module 3 Overview

Lesson 1: Applying Data Mining to Real-World Problems Part 1

Lesson 2: Applying Data Mining to Real-World Problems Part 2

Lesson 3: Comparing AI Performance to Clinician Performance Part 1

Lesson 4: Analyzing the EAGLE Study

Lesson 5: Comparing AI Performance to Clinician Performance Part 2

Module 3 Wrap-Up

Module 4 Overview

Lesson 1: Why Clinicians Resist AI-Enabled Algorithms

Lesson 2: Addressing Validation Issues

Lesson 3: Internal/External Validation

Lesson 4: Clinical Validation Studies

Mayo Spotlight: Mayo Clinic on Health AI

Module 4 Wrap-Up