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.
Este curso faz parte do Programa de cursos integrados Practical Data Science with MATLAB
oferecido por
Informações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 22 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
Machine LearningMatlabPredictive Modelling
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 22 horas para completar
Inglês
Legendas: Inglês
oferecido por
MathWorks
Accelerating the pace of discovery, innovation, development, and learning in engineering and science.
Programa - O que você aprenderá com este curso
5 horas para concluir
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 concluir
11 vídeos (Total 73 mín.), 7 leituras, 7 testes
11 videos
Instructor Introduction2min
Introduction to Supervised Machine Learning4min
Introduction to the Taxi Data7min
Creating and Cleaning Features8min
Introduction to Regression8min
Using the Regression Learner App10min
Customizing Model Parameters9min
Evaluating Regression Models6min
Evaluate Your Model in MATLAB9min
Summary of Regression1min
7 leituras
Download and Install MATLAB15min
Data and Code Files15min
Supervised Machine Learning Reference10min
Introduction to Module 15min
Variables in the Taxi Data10min
Summary of Regression Models15min
Regression Metrics10min
3 exercícios práticos
Feature Engineering Review12min
Train a Regression Model30min
Apply the Regression Workflow45min
4 horas para concluir
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 concluir
6 vídeos (Total 45 mín.), 6 leituras, 2 testes
6 videos
Using the Classification Learner App7min
Evaluating Classification Models11min
Evaluating Classification Models in MATLAB5min
Training a Multiclass Model7min
Summary of Classification1min
6 leituras
Introduction to Module 25min
Summary of Classification Models15min
Binary Classification Metrics Reference20min
Evaluate and Customize Classification Models30min
Multiclass Classification Metrics Reference20min
Customizing Multiclass Models30min
2 exercícios práticos
Train a Classification Model30min
Apply The Classification Workflow50min
8 horas para concluir
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 concluir
9 vídeos (Total 49 mín.), 5 leituras, 3 testes
9 videos
Using Validation Data During Training3min
Embedded Methods for Feature Selection7min
Using Regularization to Prevent Overfitting6min
Introduction to Ensemble Models3min
Training Ensemble Models3min
Introduction to Hyperparameters5min
Optimizing Hyperparameters8min
Summary of Module 32min
5 leituras
Introduction to Module 310min
Examining Bias Variance Trade-off15min
Practice Partitioning Data30min
Using Wrapper Methods to Select Features40min
Introduction to the Course Project10min
2 exercícios práticos
Practice Reducing Model Complexity30min
Applying Ensemble Models30min
5 horas para concluir
Advanced Topics and Next Steps
5 horas para concluir
5 vídeos (Total 26 mín.), 7 leituras, 2 testes
5 videos
Reducing Specific Errors Using Cost Matrices7min
Integrating Your Model3min
A Discussion with Heather7min
Summary of Predictive Modeling and Machine Learning3min
7 leituras
Introduction to Module 45min
Sampling Data30min
Practice Handling Class Imbalance30min
Oversampling the Minority Class30min
Examples of Integrating Machine Learning Models15min
Automated Machine Learning45min
Provide Feedback on Your Course Experience5min
2 exercícios práticos
Practice Reducing Prediction Errors30min
Quiz: Advanced Topics and Next Steps1h
Avaliações
Principais avaliações do PREDICTIVE MODELING AND MACHINE LEARNING WITH MATLAB
por SS13 de Set de 2020
Extremely helpful and interesting course. The basic concepts and further learnings are well addressed by the instructors.\n\nHighly recommended course for Machine Learning.
por AM6 de Nov de 2020
Outstanding course with real practical study case and easy to understand approach to build ML models and deploy it for production for end-user. Good job MathWorks.
por AH10 de Set de 2020
Very practical, but still high-level view to manage such projects. Testing was sufficient to test a full understanding. Thanks, I learnt a lot.
por AT16 de Dez de 2020
Learner friendly explanations and examples! The animations complement the great content!
Sobre Programa de cursos integrados 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?
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