Acerca de este Curso

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Certificado para compartir
Obtén un certificado al finalizar
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. 63 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Qué aprenderás

  • The concept of various machine learning algorithms.

  • How to apply machine learning models on datasets with Python in Jupyter Notebook.

  • How to evaluate machine learning models.

  • How to optimize machine learning models.

Habilidades que obtendrás

Text AnalysisBasic Time Series AnalysisMachine Learning Model Evaluation and OptimizationPython ProgrammingMachine Learning Modeling
Certificado para compartir
Obtén un certificado al finalizar
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. 63 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

ofrecido por

Logotipo de Universidad de Illinois en Urbana-Champaign

Universidad de Illinois en Urbana-Champaign

Comienza a trabajar para obtener tu maestría

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

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

1 hora para completar

INTRODUCTION TO THE COURSE

1 hora para completar
2 videos (Total 9 minutos), 3 lecturas
2 videos
About Linden Lu3m
3 lecturas
Syllabus10m
Glossary10m
Update Your Profile10m
8 horas para completar

MODULE 1: INTRODUCTION TO MACHINE LEARNING

8 horas para completar
4 videos (Total 24 minutos), 1 lectura, 2 cuestionarios
4 videos
1.1 Introduction to Machine Learning6m
1.2 Introduction to Data Preprocessing10m
1.3 Introduction to Machine Learning Algorithms3m
1 lectura
Module 1 Overview and Resources10m
1 ejercicio de práctica
Module 1 Quiz20m
Semana
2

Semana 2

8 horas para completar

MODULE 2: FUNDAMENTAL ALGORITHMS I

8 horas para completar
4 videos (Total 31 minutos), 1 lectura, 2 cuestionarios
4 videos
2.1 Introduction to Linear Regression12m
2.2 Introduction to Logistic Regression8m
2.3 Introduction to Decision Tree6m
1 lectura
Module 2 Overview and Resources10m
1 ejercicio de práctica
Module 2 Quiz20m
Semana
3

Semana 3

8 horas para completar

MODULE 3: Fundamental Algorithms II

8 horas para completar
4 videos (Total 15 minutos), 1 lectura, 2 cuestionarios
4 videos
3.1 Introduction to K-nearest Neighbors5m
3.2 Introduction to Support Vector Machine4m
3.3 Introduction to Bagging and Random Forest3m
1 lectura
Module 3 Overview and Resources10m
1 ejercicio de práctica
Module 3 Quiz20m
Semana
4

Semana 4

8 horas para completar

MODULE 4: MODEL EVALUATION

8 horas para completar
4 videos (Total 31 minutos), 1 lectura, 2 cuestionarios
4 videos
4.1 Regressive Evaluation Metrics8m
4.2 Classification Evaluation Metrics I13m
4.3 Classification Evaluation Metrics II7m
1 lectura
Module 4 Overview and Resources10m
1 ejercicio de práctica
Module 4 Quiz20m

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