scikit-learn Cookbook – Second Edition 版本: Over 80 recipes for machine learning in Python with scikit-learn
By 作者: Julian Avila
ISBN-10 书号: 178728638X
ISBN-13 书号: 9781787286382
Edition 版本: 2nd Revised edition
Release Finelybook 出版日期: 2017-11-16
pages 页数: (374 )

$39.99

Book Description to Finelybook sorting

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.
The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model.
By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.
Contents
1: HIGH-PERFORMANCE MACHINE LEARNING – NUMPY
2: PRE-MODEL WORKFLOW AND PRE-PROCESSING
3: DIMENSIONALITY REDUCTION
4: LINEAR MODELS WITH SCIKIT-LEARN
5: LINEAR MODELS – LOGISTIC REGRESSION
6: BUILDING MODELS WITH DISTANCE METRICS
7: CROSS-VALIDATION AND POST-MODEL WORKFLOW
8: SUPPORT VECTOR MACHINES
9: TREE ALGORITHMS AND ENSEMBLES
10: TEXT AND MULTICLASS CLASSIFICATION WITH SCIKIT-LEARN
11: NEURAL NETWORKS
12: CREATE A SIMPLE ESTIMATOR
What You Will Learn
Build predictive models in minutes by using scikit-learn
Understand the differences and relationships between Classification and Regression, two types of Supervised Learning.
Use distance metrics to predict in Clustering, a type of Unsupervised Learning
Find points with similar characteristics with Nearest Neighbors.
Use automation and cross-validation to find a best model and focus on it for a data product
Choose among the best algorithm of many or use them together in an ensemble.
Create your own estimator with the simple syntax of sklearn
Explore the feed-forward neural networks available in scikit-learn
Authors
Julian Avila
Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in mathematics, where he researched quantum mechanical computation, a field involving physics, math, and computer science. While at MIT, Julian first picked up classical and flamenco guitars, Machine Learning, and artificial intelligence through discussions with friends in the CSAIL lab.
He started programming in middle school, including games and geometrically artistic animations. He competed successfully in math and programming and worked for several groups at MIT. Julian has written complete software projects in elegant Python with just-in-time compilation. Some memorable projects of his include a large-scale facial recognition system for videos with neural networks on GPUs, recognizing parts of neurons within pictures, and stock-market trading programs.
Trent Hauck
Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.

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