Applied Unsupervised Learning with Python: Discover hidden patterns and relationships in unstructured data with Python

Book Description

Unsupervised learning is a useful and practical solution in situations where labeled data is not available.

Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course 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. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises.

By the end of this course, you will have the skills you need to confidently build your own models using Python.

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 analyse 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

Who this book is 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.

Table of Contents

1. Introduction to Clustering
2. Hierarchical Clustering
3. Neighborhood Approaches and DBSCAN
4. An Introduction to Dimensionality Reduction and PCA
5. Autoencoders
6. t-Distributed Stochastic Neighbor Embedding (t-SNE)
7. Topic Modeling
8. Market Basket Analysis
9. Hotspot Analysis