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.
Offert par
IBMÀ propos de ce cours
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Niveau intermédiaire
Approx. 9 heures pour terminer
Anglais
Sous-titres : Anglais
Compétences que vous acquerrez
Dimensionality ReductionUnsupervised LearningCluster AnalysisK Means ClusteringPrincipal Component Analysis (PCA)
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 9 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
IBM
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Programme du cours : ce que vous apprendrez dans ce cours
2 heures pour terminer
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.
2 heures pour terminer
11 vidéos (Total 62 min), 2 lectures, 3 quiz
4 heures pour terminer
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.
4 heures pour terminer
16 vidéos (Total 143 min), 3 lectures, 4 quiz
4 heures pour terminer
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.
4 heures pour terminer
9 vidéos (Total 88 min), 3 lectures, 4 quiz
Avis
Meilleurs avis pour APPRENTISSAGE NON SUPERVISÉ
par AF6 nov. 2020
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
par NW25 déc. 2020
Great course for learning about Unsupervised Learning
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