Machine Learning Pocket Reference: Working with Structured Data in Python
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Description
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. Youâ ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Specifications
| Dimensions | 11.43 x 1.91 x 17.78 cm |
| Edition | Revised |
| Isbn 10 | 1492047546 |
| Isbn 13 | 978-1492047544 |
| Item Weight | 1.05 Kilograms |
| Language | English |
| Print Length | 318 pages |
| Publication Date | 30 September 2019 |
| Publisher | O'Reilly Media |
Reviews
User
Really useful.
Already had a lot of use, and will get much more.
User
Una guía útil
Aun tratándose simplemente de un manual de bolsillo, introduce el uso de bibliotecas recientes como yellowbrick que no aparecen en libros mucho más profundos sobre la materia. Es un librito interesante, que está bien tener para refrescar conceptos de vez en cuando con ejemplos claros, sencillos y que funcionan perfectamente. Si eres completamente nuevo en la materia, no te servirá de gran ayuda. Es más bien para lectores que ya tienen algún conocimiento sobre el mundo del aprendizaje automático. Como complemento de otras lecturas está muy bien.
User
A very useful reference and learning tool
Great reference with very useful cases and applications. It lacks deep learning but it is otherwise very comprehensive. I wish there will be a similar one dedicated to deep learning.
User
Livro prático, com exemplos e insights interessantes a nível de código
Muito bom o livro, direto ao ponto com explicação clara dos conceitos e code snippets fornecidos. Sugere abordagens interessantes envolvendo as principais bibliotecas do Python para ML, capazes de acelerar muito todo o processo de modelagem. Peca apenas na qualidade física talvez por ser muito pequeno o livro e frágil.
User
Useful quick reference packed with helpful code
Machine Learning is a large domain and a book covering this topic needs to choose carefully what to cover. In Machine Learning Pocket Reference, the author chooses to focus on processing structured data. This means he avoids discussing neural network libraries such as TensorFlow or Natural Language Processing tools like spaCy or NLTK. This conscious decision means he can focus on clear and detailed code examples for solving traditional classification or regression problems using scikit-learn (and other python tools). Each chapter uses concise code samples to walk through how to use many different python packages to work through the multiple steps of a typical machine learning problem. This book is best for someone that has a little bit of exposure to python, pandas and scikit-learn and wishes to learn how to use these tools effectively. It also provides a very good introduction to about 36 other python libraries commonly used in the data science field.If you find yourself looking for quick reminders of how to use functions or are interested in multiple approaches to solving a python data science problem, this book will be a great addition to your bookshelf (real or virtual).One quick note about the size of the book. It is a quick reference so it is suitable for carrying around in your laptop bag. I was a little surprised about the size, so I am attaching an image so you can get a sense for the dimensions of the book.
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