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

Applied Unsupervised Learning with Python: Discover hidden patterns and relationships in unstructured data with Python
Applied Unsupervised Learning with Python: Discover hidden patterns and relationships in unstructured data with Python by Benjamin Johnston
English | 2019 | ISBN: 1789952292 | 482 Pages | EPUB | 239 MB

Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data
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