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.
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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
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
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
11 vidéos
Course Introduction1 min
Introduction to Unsupervised Learning - Part 18 min
Introduction to Unsupervised Learning - Part 24 min
Introduction to Clustering1 min
K-Means - Part 13 min
K-Means - Part 23 min
K-Means - Part 34 min
K-Means - Part 44 min
K Means Notebook - Part 19 min
K Means Notebook - Part 26 min
K Means Notebook - Part 313 min
2 lectures
K Means Demo (Activity)10 min
Summary10 min
3 exercices pour s'entraîner
Introduction to Unsupervised Learning10 min
K Means Clustering10 min
End of Module20 min
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
16 vidéos
Distance Metrics - Part 26 min
Curse of Dimensionality Notebook - Part 111 min
Curse of Dimensionality Notebook - Part 212 min
Curse of Dimensionality Notebook - Part 312 min
Curse of Dimensionality Notebook - Part 410 min
Hierarchical Agglomerative Clustering - Part 13 min
Hierarchical Agglomerative Clustering - Part 29 min
DBSCAN - Part 15 min
DBSCAN - Part 28 min
Mean Shift9 min
Comparing Algorithms11 min
Clustering Notebook - Part 114 min
Clustering Notebook - Part 26 min
Clustering Notebook - Part 37 min
Clustering Notebook - Part 411 min
3 lectures
Curse of Dimensionality Demo (Activity)10 min
Clustering Demo (Activity)10 min
Summary10 min
4 exercices pour s'entraîner
Distance Metrics10 min
Clustering Algorithms10 min
Comparing Clustering Algorithms10 min
End of Module10 min
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
9 vidéos
Dimensionality Reduction - Part 26 min
PCA Notebook - Part 111 min
PCA Notebook - Part 212 min
PCA Notebook - Part 311 min
Non Negative Matrix Factorization9 min
Non Negative Matrix Factorization Notebook - Part 18 min
Non Negative Matrix Factorization Notebook - Part 26 min
Dimensionality Reduction Imaging Example7 min
3 lectures
Principal Component Analysis (Activity)2 min
Non Negative Matrix Factorization (Activity)2 min
Summary10 min
3 exercices pour s'entraîner
Dimensionality Reduction5 min
Non Negative Matrix Factorization10 min
End of Module10 min
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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