Learn how to select the most suitable Python library to solve your problem
Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them
Explore the applications of neural networks using real-world datasets

Description

Unsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products. By the end of this book, you will have the skills you need to confidently build your own models using Python.

Who is this book for?

This course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.

What you will learn

Understand the basics and importance of clustering
Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages
Explore dimensionality reduction and its applications
Use scikit-learn (sklearn) to implement and analyze principal component analysis (PCA) on the Iris dataset
Employ Keras to build autoencoder models for the CIFAR-10 dataset
Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data

Estimated delivery fee Deliver to Canada

Economy delivery 10 - 13 business days

Can$24.95

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Frequently bought together

Can$55.99

Hands-On Unsupervised Learning with Python

Can$65.99

Can$61.99

Total Can$ 183.97

FAQs

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