This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
oferecido por
IBMInformações sobre o curso
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Certificados compartilháveis
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100% on-line
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Nível intermediário
Aprox. 13 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
- Dimensionality Reduction
- Unsupervised Learning
- Cluster Analysis
- K Means Clustering
- Principal Component Analysis (PCA)
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. 13 horas para completar
Inglês
Legendas: Inglês
oferecido por
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Programa - O que você aprenderá com este curso
3 horas para concluir
Introduction to Unsupervised Learning and K Means
This module introduces Unsupervised Learning and its applications. One of the most common uses of Unsupervised Learning is clustering observations using k-means. In this module you become familiar with the theory behind this algorithm, and put it in practice in a demonstration.
3 horas para concluir
11 vídeos (Total 62 mín.), 1 leitura, 4 testes
5 horas para concluir
Selecting a clustering algorithm
In this module you become familiar with some of the computational hurdles around clustering algorithms, and how different clustering implementations try to overcome them. After a brief recapitulation of common clustering algorithms, you will learn how to compare them and select the clustering technique that best suits your data.
5 horas para concluir
16 vídeos (Total 143 mín.), 1 leitura, 6 testes
5 horas para concluir
Dimensionality Reduction
This module introduces dimensionality reduction and Principal Component Analysis, which are powerful techniques for big data, imaging, and pre-processing data. At the end of this module, you will have all the tools in your toolkit to highlight your Unsupervised Learning abilities in your final project.
5 horas para concluir
9 vídeos (Total 88 mín.), 2 leituras, 5 testes
Avaliações
- 5 stars81,56%
- 4 stars12,76%
- 3 stars2,83%
- 2 stars2,12%
- 1 star0,70%
Principais avaliações do UNSUPERVISED MACHINE LEARNING
por MB22 de abr de 2021
A high quality course with lots of practical techniques
por AD18 de abr de 2021
It is a beautifully crafted course that looks at various clustering algorithms. More importantly, show the pros and cons of each algorithm/technique based on different patterns.
por KD11 de abr de 2022
Awesome and wholesome explaination of the concepts
por AF6 de nov de 2020
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
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