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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Certificat partageable
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100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau intermédiaire
Approx. 9 heures pour terminer
Anglais
Sous-titres : Anglais
Compétences que vous acquerrez
- Dimensionality Reduction
- Unsupervised Learning
- Cluster Analysis
- K Means Clustering
- Principal Component Analysis (PCA)
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau intermédiaire
Approx. 9 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
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.
Programme de 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 UNSUPERVISED MACHINE LEARNING
par AD18 avr. 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.
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 AV5 juil. 2021
Great course. Maybe there is one instance of wrong answer in one of the quizzes. Everything elese is perfect. Thanks IBM !
par MB22 avr. 2021
A high quality course with lots of practical techniques
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