Before we talk about agglomerative clustering, we need to understand hierarchical clustering. Hierarchical clustering refers to a set of clustering algorithms that creates tree-like clusters by consecutively splitting or merging them, and they are represented using a tree. Hierarchical clustering algorithms can be either bottom-up or top-down. Now, what does this mean? In bottom-up algorithms, each datapoint is treated as a separate cluster with a single object. These clusters are then successively merged until all the clusters are merged into a single giant cluster. This is called agglomerative clustering. On the other hand, top-down algorithms start with a giant cluster and successively split these clusters until individual datapoints are reached.
![Book Image](https://content.packt.com/B12585/cover_image_small.jpg)
Python Machine Learning Cookbook, - Second Edition
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![Book Image](https://content.packt.com/B12585/cover_image_small.jpg)
Python Machine Learning Cookbook, - Second Edition
By:
Overview of this book
This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.
With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.
By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.
Table of Contents (18 chapters)
Preface
The Realm of Supervised Learning
Constructing a Classifier
Predictive Modeling
Clustering with Unsupervised Learning
Visualizing Data
Building Recommendation Engines
Analyzing Text Data
Speech Recognition
Dissecting Time Series and Sequential Data
Analyzing Image Content
Biometric Face Recognition
Reinforcement Learning Techniques
Deep Neural Networks
Unsupervised Representation Learning
Automated Machine Learning and Transfer Learning
Unlocking Production Issues
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