One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
oferecido por
Universidade Johns HopkinsInformações sobre o curso
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Aprox. 9 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Coreano, Alemão, Russo, Inglês, Espanhol
O que você vai aprender
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Habilidades que você terá
- Random Forest
- Machine Learning (ML) Algorithms
- Machine Learning
- R Programming
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Aprox. 9 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Coreano, Alemão, Russo, Inglês, Espanhol
oferecido por
Universidade Johns Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Programa - O que você aprenderá com este curso
2 horas para concluir
Week 1: Prediction, Errors, and Cross Validation
This week will cover prediction, relative importance of steps, errors, and cross validation.
2 horas para concluir
9 vídeos (Total 73 mín.), 4 leituras, 1 teste
2 horas para concluir
Week 2: The Caret Package
This week will introduce the caret package, tools for creating features and preprocessing.
2 horas para concluir
9 vídeos (Total 96 mín.)
1 hora para concluir
Week 3: Predicting with trees, Random Forests, & Model Based Predictions
This week we introduce a number of machine learning algorithms you can use to complete your course project.
1 hora para concluir
5 vídeos (Total 48 mín.)
3 horas para concluir
Week 4: Regularized Regression and Combining Predictors
This week, we will cover regularized regression and combining predictors.
3 horas para concluir
4 vídeos (Total 33 mín.), 2 leituras, 3 testes
Avaliações
- 5 stars66,40%
- 4 stars22,38%
- 3 stars6,93%
- 2 stars2,49%
- 1 star1,77%
Principais avaliações do APRENDIZAGEM AUTOMÁTICA NA PRÁTICA
por AS30 de ago de 2017
Highly recommend this course. It makes you read a lot, do lot's of practical exercises. The final project is a must do. After finishing this course you can start playing with kaggle data sets.
por CJ11 de mai de 2018
This course was really informative and extremely efficient by letting you know just the few basics needed to build some quite advanced models such as random forest..
por JF13 de fev de 2019
Very good course. Clear explanations and examples give a good overview of the foundations of Machine Learning. After this course the student can build Machine Learning models.
por NK18 de fev de 2016
Some of the terms used here vary from the terms used in the industry. For example recall, precision etc. Overall this is a very good course with provides basics of machine learning.
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