Machine Learning Pocket Reference Working with Structured Data in Python
Langue : Anglais
Auteur : Harrison Matt
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.
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
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. You’ll also learn methods for
clustering, predicting a continuous value (regression), and reducing
dimensionality, among other topics.
Matt Harrison is a Python user, presenter, author, and user group organizer. He helps run the Utah Python user group. He authored the best-selling Treading on Python Vol 1 & 2 books. He runs MetaSnake, a Python training and consultancy shop.
Date de parution : 09-2019
Ouvrage de 320 p.
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
Prix indicatif 28,20 €
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