This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.
Este curso faz parte do Programa de cursos integrados Machine Learning: Algorithms in the Real World
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Programa de cursos integrados Machine Learning: Algorithms in the Real WorldAlberta Machine Intelligence InstituteInformações sobre o curso
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Prazos flexíveis
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
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Nível intermediário
Aprox. 7 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
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.
Nível intermediário
Aprox. 7 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
oferecido por
Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
Programa - O que você aprenderá com este curso
3 horas para concluir
Introduction to Machine Learning Applications
This week, you will learn about what machine learning (ML) actually is, contrast different problem scenarios, and explore some common misconceptions about ML. You will apply this knowledge by identifying different components essential to a machine learning business solution.
3 horas para concluir
12 vídeos (Total 44 mín.), 6 leituras, 2 testes
2 horas para concluir
Machine Learning in the Real World
This week, you will learn how to translate a business need into a machine learning problem. We'll walk through some applied examples so you can get a feel for what makes a well-defined question for your QuAM. Narrowing down your question and making sure you have the data necessary to learn is critical to ML success!
2 horas para concluir
8 vídeos (Total 34 mín.), 4 leituras, 1 teste
1 hora para concluir
Learning Data
This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.
1 hora para concluir
9 vídeos (Total 34 mín.), 2 leituras, 1 teste
1 hora para concluir
Machine Learning Projects
This week you will learn about the Machine Learning Process Lifecycle (MLPL). After understanding the definitions and components of the MLPL you will analyze the application of the MLPL on a case study.
1 hora para concluir
7 vídeos (Total 35 mín.), 2 leituras, 1 teste
Avaliações
- 5 stars74,53%
- 4 stars19,85%
- 3 stars4,60%
- 2 stars0,28%
- 1 star0,71%
Principais avaliações do INTRODUCTION TO APPLIED MACHINE LEARNING
por CW14 de set de 2020
The lectures are very clear and easy to follow. More importantly, it gives me a big picture of how Machine Learning can be applied to the real-world business.
por BT5 de jan de 2020
Course gives a broader understanding of any Machine Learning project, how to approach and what are the important things to keep in mind.
por AA11 de set de 2019
very comprehensive course on applied machine learning. the most interesting information in this course is business needs for ML and what it's requirement to have a good QuAM.
por KS13 de out de 2019
This course will give the actual understanding especially focusing on different types of Machine learning with real examples. I'm so excited to learn more about it.
Sobre Programa de cursos integrados Machine Learning: Algorithms in the Real World
This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application.
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