Acerca de este Curso
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100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 13 horas para completar

Sugerido: 4-6 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
8 horas para completar

Module 0 Get Ready & Module 1 Drowning in Data, Starving for Knowledge

This module will introduce you to the most common and important unsupervised learning technique – Clustering. You will have an understanding of different applications of clustering analysis after this module. And we would let you know when we need clustering and why it is important. Then, you will be introduced to a variety of clustering methods.

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13 videos (Total 98 minutos), 10 readings, 4 quizzes
13 videos
Meet Professor Sridhar Seshadri1m
Rattle Installation Guidelines for Windows6m
Rattle Installation Guideline for MacOS11m
Rattle Interface for Windows9m
Lecture 1-1: Introduction to Clustering11m
Lecture 1-2: Applications of Clustering7m
Lecture 1-3: How to Cluster10m
Lecture 1-4: Introduction to K Means8m
Lecture 1-5: Hierarchical (Agglomerative) Clustering8m
Lecture 1-6: Measuring Similarity Between Clusters10m
Lecture 1-7: Real World Clustering Example6m
Lecture 1-8: Clustering Practice and Summary3m
10 lecturas
Syllabus30m
About the Discussion Forums10m
Glossary10m
Brand Descriptions10m
Update Your Profile10m
Module 0 Agenda10m
Rattle Tutorials (Interface, Windows, Mac)30m
Module 1 Overview20m
Module 1 Readings, Data Sets, and Slides1h 30m
Module 1 Peer Review Assignment Answer Key10m
3 ejercicios de práctica
Orientation Quiz10m
Module 1 Practice Problems10m
Module 1 Graded Quiz30m
Semana
2
5 horas para completar

Module 2 Decision Trees

In this module, we will discuss how to use decision trees to represent knowledge. The module concludes with a presentation of the Random Forest method that overcomes some of the limitations (such as high variance or low precision) of a single decision tree constructed from data.

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7 videos (Total 65 minutos), 3 readings, 3 quizzes
7 videos
Lecture 2-2: Model Complexity7m
Lecture 2-3: Rule Based Classifiers9m
Lecture 2-4: Entropy and Decision Trees14m
Lecture 2-5: Classification Tree Example7m
Lecture 2-6: Regression Tree Example8m
Lecture 2-7: Introduction to Forests and Spam Filter Exercise9m
3 lecturas
Module 2 Overview20m
Module 2 Readings, Data Sets, and Slides30m
Module 2 Peer Review Assignment Answer Key10m
2 ejercicios de práctica
Module 2 Practice Problems
Module 2 Graded Quiz30m
Semana
3
5 horas para completar

Module 3 Rules, Rules and More Rules

This module will focus on three key topics, namely rules, nearest neighbor methods, and Bayesian methods. Over this module, you will be exposed to how rules factor into the world of data, and how they play a role in the analysis of data. The second and third topic focus on the classification of data.

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8 videos (Total 65 minutos), 3 readings, 3 quizzes
8 videos
Lecture 3-2: K-Nearest Neighbor9m
Lecture 3-3: K-Nearest Neighbor Classifier3m
Lecture 3-4: Selecting the Best K in Rstudio12m
Lecture 3-5: Bayes' Rule7m
Lecture 3-6: The Naïve Bayes Trick13m
Lecture 3-7: Employee Attrition Example5m
Lecture 3-8: Employee Attrition Example in Rstudio, Exercise, and Summary9m
3 lecturas
Module 3 Overview20m
Module 3 Readings, Data Sets, and Slides30m
Module 3 Peer Review Assignment Answer Key10m
2 ejercicios de práctica
Module 3 Practice Problems10m
Module 3 Graded Quiz30m
Semana
4
4 horas para completar

Module 4 Model Performance and Recommendation Systems

Over this module, you will study tools for recognizing what to recommend, identify cross-sell or upsell opportunities. As the last module of the course, we will warp up the contents so far and you will get an opportunity to practice on your own and learn how to adapt these models to drive business impact in your own organizations.

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8 videos (Total 68 minutos), 3 readings, 3 quizzes
8 videos
Lecture 4-2: Classification Tree Example11m
Lecture 4-3: True and False Negatives8m
Lecture 4-4: Clock Example Exercise2m
Lecture 4-5: Making Recommendations13m
Lecture 4-6: Association Rule Mining6m
Lecture 4-7: Collaborative Filtering7m
Lecture 4-8: Recommendation Example in Rstudio and Summary12m
3 lecturas
Module 4 Overview20m
Module 4 Readings, Data Sets, and Slides1h
Module 4 Peer Review Assignment Answer Key10m
2 ejercicios de práctica
Module 4 Practice Problems10m
Module 4 Graded Quiz30m

Instructores

Avatar

Sridhar Seshadri

Professor of Business Administration
Business Administration

Comienza a trabajar para obtener tu maestría

Este curso es parte del Master of Business Administration (iMBA) completamente en línea de Universidad de Illinois en Urbana-Champaign. Si eres aceptado en el programa completo, tus cursos cuentan para tu título.

Acerca de Universidad de Illinois en Urbana-Champaign

The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs. ...

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.

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