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.
ofrecido por
IBMAcerca de este Curso
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Certificado para compartir
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Fechas límite flexibles
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Nivel intermedio
Aprox. 9 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Habilidades que obtendrás
Dimensionality ReductionUnsupervised LearningCluster AnalysisK Means ClusteringPrincipal Component Analysis (PCA)
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 9 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
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.
Programa - Qué aprenderás en este curso
2 horas para completar
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 horas para completar
11 videos (Total 62 minutos), 2 lecturas, 3 cuestionarios
11 videos
Introduction to Unsupervised Learning - Part 18m
Introduction to Unsupervised Learning - Part 24m
Introduction to Clustering1m
K-Means - Part 13m
K-Means - Part 23m
K-Means - Part 34m
K-Means - Part 44m
K Means Notebook - Part 19m
K Means Notebook - Part 26m
K Means Notebook - Part 313m
2 lecturas
K Means Demo (Activity)10m
Summary10m
3 ejercicios de práctica
Introduction to Unsupervised Learning10m
K Means Clustering10m
End of Module20m
4 horas para completar
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 horas para completar
16 videos (Total 143 minutos), 3 lecturas, 4 cuestionarios
16 videos
Distance Metrics - Part 26m
Curse of Dimensionality Notebook - Part 111m
Curse of Dimensionality Notebook - Part 212m
Curse of Dimensionality Notebook - Part 312m
Curse of Dimensionality Notebook - Part 410m
Hierarchical Agglomerative Clustering - Part 13m
Hierarchical Agglomerative Clustering - Part 29m
DBSCAN - Part 15m
DBSCAN - Part 28m
Mean Shift9m
Comparing Algorithms11m
Clustering Notebook - Part 114m
Clustering Notebook - Part 26m
Clustering Notebook - Part 37m
Clustering Notebook - Part 411m
3 lecturas
Curse of Dimensionality Demo (Activity)10m
Clustering Demo (Activity)10m
Summary10m
4 ejercicios de práctica
Distance Metrics10m
Clustering Algorithms10m
Comparing Clustering Algorithms10m
End of Module10m
4 horas para completar
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 horas para completar
9 videos (Total 88 minutos), 3 lecturas, 4 cuestionarios
9 videos
Dimensionality Reduction - Part 26m
PCA Notebook - Part 111m
PCA Notebook - Part 212m
PCA Notebook - Part 311m
Non Negative Matrix Factorization9m
Non Negative Matrix Factorization Notebook - Part 18m
Non Negative Matrix Factorization Notebook - Part 26m
Dimensionality Reduction Imaging Example7m
3 lecturas
Principal Component Analysis (Activity)2m
Non Negative Matrix Factorization (Activity)2m
Summary10m
3 ejercicios de práctica
Dimensionality Reduction5m
Non Negative Matrix Factorization10m
End of Module10m
Reseñas
Principales reseñas sobre APRENDIZAJE NO SUPERVISADO
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.
por NW25 de dic. de 2020
Great course for learning about Unsupervised Learning
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