Este curso forma parte de Programa Especializado - Practical Data Science with MATLAB

Heather Gorr +10 instructores más

ofrecido por

Programa Especializado - Practical Data Science with MATLABMathWorks

Acerca de este Curso

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In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do.
These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed courses 1 through 2 of this specialization. 

By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

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Curso 3 de 4 en

Programa Especializado - Practical Data Science with MATLAB

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 22 horas para completar

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

Machine LearningMatlabPredictive Modelling

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 3 de 4 en

Programa Especializado - Practical Data Science with MATLAB

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 22 horas para completar

Inglés (English)

Subtítulos: Inglés (English)

Instructores

Calificación del instructor

4.78/5 (28 calificaciones)

ofrecido por

MathWorks

Accelerating the pace of discovery, innovation, development, and learning in engineering and science.

Programa - Qué aprenderás en este curso

Semana

Semana 1

5 horas para completar

Creating Regression Models

In this module you'll apply the skills gained from the first two courses in the specialization on a new dataset. You'll be introduced to the Supervised Machine Learning Workflow and learn key terms. You'll end the module by creating and evaluating regression machine learning models.

5 horas para completar

11 videos (Total 73 minutos), 7 lecturas, 7 cuestionarios

11 videos

Practical Data Science with MATLAB3m

Instructor Introduction2m

Introduction to Supervised Machine Learning4m

Introduction to the Taxi Data7m

Creating and Cleaning Features8m

Introduction to Regression8m

Using the Regression Learner App10m

Customizing Model Parameters9m

Evaluating Regression Models6m

Evaluate Your Model in MATLAB9m

Summary of Regression1m

7 lecturas

Download and Install MATLAB15m

Data and Code Files15m

Supervised Machine Learning Reference10m

Introduction to Module 15m

Variables in the Taxi Data10m

Summary of Regression Models15m

Regression Metrics10m

3 ejercicios de práctica

Feature Engineering Review12m

Train a Regression Model30m

Apply the Regression Workflow45m

Semana

Semana 2

4 horas para completar

Creating Classification Models

In this module you'll learn the basics of classification models. You'll train several types of classification models and evaluation the results.

4 horas para completar

6 videos (Total 45 minutos), 6 lecturas, 2 cuestionarios

6 videos

Introduction to Classification9m

Using the Classification Learner App7m

Evaluating Classification Models11m

Evaluating Classification Models in MATLAB5m

Training a Multiclass Model7m

Summary of Classification1m

6 lecturas

Introduction to Module 25m

Summary of Classification Models15m

Binary Classification Metrics Reference20m

Evaluate and Customize Classification Models30m

Multiclass Classification Metrics Reference20m

Customizing Multiclass Models30m

2 ejercicios de práctica

Train a Classification Model30m

Apply The Classification Workflow50m

Semana

Semana 3

8 horas para completar

Applying the Supervised Machine Learning Workflow

In this module you'll apply the complete supervised machine learning workflow. You'll use validation data inform model creation. You'll apply different feature selection techniques to reduce model complexity. You'll create ensemble models and optimize hyperparameters. At the end of the module, you'll apply these concepts to a final project.

8 horas para completar

9 videos (Total 49 minutos), 5 lecturas, 3 cuestionarios

9 videos

Addressing Underfitting and Overfitting8m

Using Validation Data During Training3m

Embedded Methods for Feature Selection7m

Using Regularization to Prevent Overfitting6m

Introduction to Ensemble Models3m

Training Ensemble Models3m

Introduction to Hyperparameters5m

Optimizing Hyperparameters8m

Summary of Module 32m

5 lecturas

Introduction to Module 310m

Examining Bias Variance Trade-off15m

Practice Partitioning Data30m

Using Wrapper Methods to Select Features40m

Introduction to the Course Project10m

2 ejercicios de práctica

Practice Reducing Model Complexity30m

Applying Ensemble Models30m

Semana

Semana 4

5 horas para completar

Advanced Topics and Next Steps

5 horas para completar

5 videos (Total 26 minutos), 7 lecturas, 2 cuestionarios

5 videos

Handling Class Imbalance5m

Reducing Specific Errors Using Cost Matrices7m

Integrating Your Model3m

A Discussion with Heather7m

Summary of Predictive Modeling and Machine Learning3m

7 lecturas

Introduction to Module 45m

Sampling Data30m

Practice Handling Class Imbalance30m

Oversampling the Minority Class30m

Examples of Integrating Machine Learning Models15m

Automated Machine Learning45m

Provide Feedback on Your Course Experience5m

2 ejercicios de práctica

Practice Reducing Prediction Errors30m

Quiz: Advanced Topics and Next Steps1h

Acerca de Programa especializado: Practical Data Science with MATLAB

Do you find yourself in an industry or field that increasingly uses data to answer questions? Are you working with an overwhelming amount of data and need to make sense of it? Do you want to avoid becoming a full-time software developer or statistician to do meaningful tasks with your data?

Completing this specialization will give you the skills and confidence you need to achieve practical results in Data Science quickly. Being able to visualize, analyze, and model data are some of the most in-demand career skills from fields ranging from healthcare, to the auto industry, to tech startups. This specialization assumes you have domain expertise in a technical field and some exposure to computational tools, such as spreadsheets. To be successful in completing the courses, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation). Throughout this specialization, you will be using MATLAB. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your data science tasks. You will be provided with free access to MATLAB for the duration of the specialization to complete your work.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. 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 auditar el curso sin costo.
Is financial aid available?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.

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Predictive Modeling and Machine Learning with MATLAB