Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Unsupervised Learning with Python

You're reading from   Hands-On Unsupervised Learning with Python Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

Arrow left icon
Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789348279
Length 386 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Unsupervised Learning FREE CHAPTER 2. Clustering Fundamentals 3. Advanced Clustering 4. Hierarchical Clustering in Action 5. Soft Clustering and Gaussian Mixture Models 6. Anomaly Detection 7. Dimensionality Reduction and Component Analysis 8. Unsupervised Neural Network Models 9. Generative Adversarial Networks and SOMs 10. Assessments 11. Other Books You May Enjoy

Questions

  1. Unsupervised learning is the most common alternative when supervised learning is not applicable. Is it correct?
  2. The CEO of your company asks you to find out the factors that determined a negative sales trend. What kind of analysis do you need to perform?

  1. Given a dataset of independent samples and a candidate data generating process (for example, a Gaussian distribution), the likelihood is obtained by summing the probabilities of all samples. It is correct?
  2. Under which hypothesis can the likelihood be computed as a product of single probabilities?
  3. Suppose we have a dataset of students containing some unknown numerical features (for example, age, marks, and so on). You want to separate male and female students, so you decide to cluster the dataset into two groups. Unfortunately, both clusters have roughly 50% male and 50% female students. How can you explain this result?
  4. Consider the previous example, but repeat the experiment and cluster into five groups. What do you expect to find in each of them? (List some reasonable possibilities.)
  5. You've clustered the customers of an online store. Given a new sample, what kind of prediction can you make?
You have been reading a chapter from
Hands-On Unsupervised Learning with Python
Published in: Feb 2019
Publisher: Packt
ISBN-13: 9781789348279
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime