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
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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
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
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100% on-line
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Aprox. 9 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.
Aprox. 9 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
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
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
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
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
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 CW29 de Set de 2020
Great course, easy to grasp the main idea of how to assess and tune the performance of question-answering machines learned by machine learning algorithms through data
por RM8 de Dez de 2020
I found the course to be enough detailed to get clarity on the basic concepts of Supervised learning algorithms. I hope to apply the learning from the course in work!
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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