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
Introduction to Applied Machine Learning
oferecido por
Programa de cursos integrados Machine Learning: Algorithms in the Real WorldAlberta Machine Intelligence Institute
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. 9 horas para completar
Sugerido: 13 hours/week
Inglês
Legendas: Inglês
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
12 videos
Instructor Introduction1min
Introduction to Course 12min
What is Artificial Intelligence and Machine Learning?5min
What about Data Science?3min
The Machine Learning Process4min
The Three Kinds of Machine Learning3min
Classification: What is it and how does it work?3min
Regression: Fitting lines and predicting numbers3min
Unsupervised Learning4min
Reinforcement Learning6min
Weekly Summary1min
6 leituras
What about Deep Learning? (supplemental)10min
Fooling Neural Networks (supplemental)10min
How to Curate A Ground Truth For Your Business Dataset (Required)10min
Learning From Multiple Annotators: A Survey (supplemental)10min
Inferring the Ground Truth Through Crowdsourcing (supplemental)10min
Semi Supervised Learning (required)10min
2 exercícios práticos
Concepts and Definitions20min
Identifying Machine Learning Techniques10min
1 hora 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!
1 hora para concluir
8 vídeos (Total 34 mín.), 4 leituras, 1 teste
8 videos
Features and transformations of raw data6min
Farmer Betty and Her Precision Agriculture Plans3min
What to consider when using your QuAM2min
Broad Examples Narrowed Down4min
Identify Business Evaluation4min
Everything is a Proxy4min
Weekly Summary2min
4 leituras
A Brief Introduction into Precision Agriculture10min
Farmer Betty Tried Unsupervised Learning (required)10min
Data is Central to Your ML Problem (required)10min
Martin Zinkevich's Rules for ML (supplemental)10min
1 exercício prático
Machine Learning in the Real World Review
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
9 videos
How Much Data Do I Need?4min
Ethical Issues4min
Bias in Data Sources3min
Noise and Sources of Randomness5min
Image Classification Example3min
Data Cleaning: Everybody's favourite task4min
Why you need to set up a Data Pipeline4min
Weekly Summary1min
2 leituras
Data Protection Laws (required)10min
Government readings on data privacy (supplemental)10min
1 exercício prático
Understanding Data for ML
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
7 videos
MLPL Overview5min
MLPL as experienced by Farmer Betty3min
Exploring the process of problem definition7min
Assessing your QuAM for use in your Business6min
Technically Assessing the Strength of your QuAM6min
Different Kinds of Wrong4min
Weekly Summary2min
2 leituras
Machine Learning Process Lifecycle Explained10min
Deep Learning for Identifying Metastatic Breast Cancer (advanced supplemental)10min
1 exercício prático
Understanding Machine Learning Projects
Avaliações
Principais avaliações do INTRODUCTION TO APPLIED MACHINE LEARNING
por MMOct 28, 2019
I have really got benefit from this course as a beginner to ML, it gives me the best understanding of ML. I m looking forward to getting into it more efficiently with more practices.
por AASep 11, 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 KSOct 13, 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.
por VESep 21, 2019
I have taken a great deal of knowledge in this course and have applied it to what I have seen in the ML and AI world. This was a great introduction!
Sobre 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
research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning.
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.
After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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