This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.
Este curso faz parte do Programa de cursos integrados Machine Learning: Algorithms in the Real World
oferecido por
Programa de cursos integrados Machine Learning: Algorithms in the Real WorldAlberta Machine Intelligence InstituteInformações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
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Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 9 horas para completar
Inglês
Legendas: Inglês
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.
Aprox. 9 horas para completar
Inglês
Legendas: Inglês
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
4 horas para concluir
Classification using Decision Trees and k-NN
Welcome to Supervised Learning, Tip to Tail! This week we'll go over the basics of supervised learning, particularly classification, as well as teach you about two classification algorithms: decision trees and k-NN. You'll get started programming on the platform through Jupyter notebooks and start to familiarize yourself with all the issues that arise when using machine learning for classification.
4 horas para concluir
8 vídeos (Total 46 mín.), 4 leituras, 2 testes
8 videos
What does a classifier actually do?5min
Classification in scikit-learn3min
What are decision trees?6min
Generalization and overfitting8min
Classification using k-nearest neighbours8min
Distance measures8min
Weekly summary2min
4 leituras
Math Review10min
Scikitlearn documentation for decision trees (Optional)10min
Scikitlearn documentation for random forests (Optional)10min
Scikitlearn documentation for k-nearest neighbours (Optional)10min
2 exercícios práticos
Supervised Learning Basics
Understanding Classification with Decision Trees and k-NN20min
2 horas para concluir
Functions for Fun and Profit
Welcome to the second week of the course! In this week you'll learn all about regression algorithms, the other side of supervised learning. We'll introduce you to the idea of finding lines, optimization criteria, and all the associated issues. Through regression we'll see the interactions between model complexity and accuracy, and you'll get a first taste of how regression and classification might relate.
2 horas para concluir
9 vídeos (Total 62 mín.), 1 leitura, 4 testes
9 videos
Line-fitting6min
Optimal line-fitting8min
Loss and Convexity7min
Gradient Descent9min
Nonlinear features and model complexity6min
Bias and variance tradeoff6min
Regularizers5min
Loss for Classification7min
Weekly summary4min
1 leituras
Scikitlearn documentation for linear regression (Optional)10min
4 exercícios práticos
Regression Basics
Understanding Model Complexity
From Regression to Classification2min
The Regression side of Supervised Learning20min
3 horas para concluir
Regression for Classification: Support Vector Machines
This week we'll be diving straight in to using regression for classification. We'll describe all the fundamental pieces that make up the support vector machine algorithms, so that you can understand how many seemingly unrelated machine learning algorithms tie together. We'll introduce you to logistic regression, neural networks, and support vector machines, and show you how to implement two of those.
3 horas para concluir
6 vídeos (Total 34 mín.), 1 leitura, 2 testes
6 videos
Neural Networks9min
Hinge Loss6min
Basics of Support Vector Machines6min
Kernels6min
Weekly Summary1min
1 leituras
Scikitlearn documentation for SVMs (Optional)10min
2 exercícios práticos
Understanding Support Vector Machines
Regression-based Classification10min
1 hora para concluir
Contrasting Models
Now at the tail end of the course, we're going to go over how to know how well your model is actually performing and what you can do to get even better performance from it. We'll review assessment questions particular to regression and classification, and introduce some other tools that really help you analyze your model performance. The topics covered this week aim to give you confidence in your models, so you're ready to unlock the power of machine learning for your business goals.
1 hora para concluir
8 vídeos (Total 46 mín.), 1 leitura, 1 teste
8 videos
Classification assessment6min
Learning Curves6min
Testing your models7min
Cross validation5min
Parameter tuning and grid search5min
Model Parameters6min
Weekly Summary1min
1 leituras
Some resources on model assessment (Optional)10min
1 exercício prático
Contrasting Models
Avaliações
Principais avaliações do MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL
por DS6 de Mai de 2020
Excellent course for an overview of different ML algorithms. The course is made from a perspective of giving insights in process and not too many mathematical details.
por SK11 de Abr de 2020
Excellent course. In which I had in-depth knowledge of all algorithms and the way she explained attracts to listen except for her spontaneity and speed in progressing.
por FF16 de Abr de 2020
Great course but less in-depth knowledge about each of the hyper parameters and under the hood view of Algorithms.But excellent. Thanks!!!!!!
por SP18 de Jun de 2020
A great short capsule course to get overall bird view on Supervised learning. Much needed one for both practitioners and new beginners.
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?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.
Qual é a política de reembolso?
Se você se inscrever, terá 7 dias para testar sem custo e, durante este período, pode cancelar sem multa. Depois disso, não reembolsamos, mas você pode cancelar sua inscrição a qualquer momento. Veja nossa política para o reembolso total.
Existe algum auxílio financeiro disponível?
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