The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

Aaron Jones

Christopher Kruger

Benjamin Johnston

€32.99

4.3 (6 Ratings)

Paperback Jul 2020 550 pages 1st Edition

Aaron Jones

Christopher Kruger

Benjamin Johnston

€32.99

4.3 (6 Ratings)

Paperback Jul 2020 550 pages 1st Edition

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Key benefits

Get familiar with the ecosystem of unsupervised algorithms
Learn interesting methods to simplify large amounts of unorganized data
Tackle real-world challenges, such as estimating the population density of a geographical area

Description

Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you’ll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you’ll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you’ll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.

Who is this book for?

If you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as you’ll be editing classes and functions instead of creating them from scratch.

What you will learn

Distinguish between hierarchical clustering and the k-means algorithm
Understand the process of finding clusters in data
Grasp interesting techniques to reduce the size of data
Use autoencoders to decode data
Extract text from a large collection of documents using topic modeling
Create a bag-of-words model using the CountVectorizer

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Amazon verified reviews

ShivamPandey Feb 23, 2021

This is an amazing book.Unsupervised Learning with Python contains comprehensive coverage of the mathematical foundations, algorithms, and practical implementations of unsupervised learning. This book provides machine learning approach to uncover patterns and trends in your data, and support sound strategic decisions for your business.The book bridges the gap between complex math and practical Python implementations. It involves Fundamental building blocks and concepts of unsupervised learning,Choosing the right algorithm for your problem & How to interpret the results of unsupervised learning.Market Basket Analysis & HotSpot Analysis were the stand outs for me. The concepts are explained in an easy to go smooth flowing pattern.I would love if the book could also provide hands on with code via video excercises. I would rate this book a 5 star & must read for individuals exploring the space of Unsupervised Machine Learning. Special call out to the author & publishers of the book. Thank you.

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Marleen Feb 19, 2021

The books starts basically right away with the first main topic: Clustering. Only a few pages of Supervised vs Unsupervised Learning serve as introduction. However, often times I find myself scanning through 3 chapters or so of intro or repetition before a book starts with what it actually is about, therefore this is a welcoming change and meant to be a positive feedback.Unsupervised Learning is known to be more difficult to implement and also to explain to e.g. Management, Audit etc. This book can definitely be used as a guide to understand various areas of unsupervised learning and within each area you will learn multiple methods that one could use as examples at work to explain a concept.I am fairly new to unsupervised learning and tend to use supervised learning as much as possible but this book definitely gives me a good base to try new things.I can see that for more advanced users this book might be limited and sometimes to high level, but for me this book has just the right depths. I also enjoyed the accompanying material and I liked that the books tried to put images wherever possible. Therefore I would rate it - for my use-cases - with 5 stars.

Julian M. - DS and ML Advisor | MSc. [c] Feb 16, 2021

The book is written with a simple vocabulary, combining technical topics with accessible content for those inexperienced in Machine Learning. The examples are theoretical-practical and always written in an understandable and easily digestible format. When reading the algorithmic details, a continuous and coherent narration of the explained techniques is presented, always maintaining the general context of unsupervised learning.The theoretical framework begins with the explanation of well-known clustering techniques such as K-means, DBSCAN, and Hierarchical clustering. The deepening of the description of K-means allows extrapolating the theoretical aspects to other more complex procedures. Consecutively, dimensionality reduction techniques are detailed, emphasizing Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) as a visualization algorithm. Decomposition by eigenvector and eigenvalues is explained through Singular Value Decomposition. The Autoencoders topic explained after PCA has an understandable introduction about several activation functions and perceptron models, explaining Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) in simple words with a theoretical grasp. Finally, the content is oriented towards (a) some components of natural language processing as an application example for techniques such as Topic Modeling, with emphasis on Latent Dirichlet Allocation (LDA); and (b) some components of inferential statistics such as the estimation of data distributions through Kernel Density Estimation and the use of Kernel functions for linear regression, among others.From a practical point of view, a notable aspect is the writers' ability to explain algorithmic methods from scratch and using Python libraries. Each technique is detailed with its practical component in Python, allowing to practice while the reader is learning the theory.My major concern is regarding the depth of the content. It is a book that allows one to grasp the general aspects of unsupervised learning as a workshop, as the title mentions. However, some Deep Learning topics, such as Convolutional Neural Networks, are not explained sufficiently rigorously. The interpretability and explainability of such models are partially ignored. If you are looking in the book for the depth of theory and open implementations, this book is not the place to be. Conversely, if looking for a tool that allows you to learn about applied unsupervised learning algorithms, implementing known techniques, and using existing libraries, the book is the right place for your learning. Although some code is incomplete for the sake of understanding and flow of the book, it can be found in the Github repository in more detail, which is referenced throughout the book.Overall, I enjoyed reading the book and highly recommend it. It covers a wide variety of unsupervised learning techniques. The balance between theory and practice is right for those looking to get a grasp and general overview of these Machine Learning topics.

Mr Critical Feb 14, 2021

I have given this book a thorough read and I have also read several other books on Data Science. One of the interesting aspects of this book is that it helps the reader understand the concept through code snippets. The book is built with good examples and robust code which means anyone can start mimicking the examples given and understand what is happening. For me personally its always best when I have some form of code for each concept.Another thing that I liked about the book is the structure given for learning. The book explains very well about unsupervised learning concepts such as Kmeans vs DBSCAN vs Heirachical clustering. Good book to have on your shelf incase you are getting into Data Science.

Darpan Jan 27, 2021

Pros:This book is written in etiquette and very attractive.Details and enough information about the methods used in unsupervised learning.This is the first time I am seeing you implement jupyter style code in each section and also output which is quite amazing.Every data scientist would like to know how this algorithm would help in real time applications where the actual data would come.I am glad that the author mentioned it.It's so amazing that visualization is also in the book which helps to understand algorithms statistically.Cons :One thing is it's length as I am reading the book chapter by chapter. It is becoming lengthy.I am not sure but every beginner has to learn each and every algorithm from scratch in terms of coding. If a person is not into the coding field, they would directly implement the library without understanding of how parameters are used.Less information about training and optimization.

The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

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