Data science from scratch : first principles with Python

Başlık:

Yazar:

Grus, Joel (Software engineer), author.

ISBN:

9781492041139

Ek Yazar:

Grus, Joel (Software engineer), author.

Edition:

Second edition.

Fiziksel Tanım:

xvii, 384 pages : illustrations ; 24 cm

Contents:

Introduction -- A crash course in Python -- Visualizing data -- Linear algebra -- Statistics -- Probability -- Hypothesis and inference -- Gradient descent -- Getting data -- Working with data -- Machine learning -- k-Nearest neighbors -- Naive bayes -- Simple linear regression -- Multiple regression -- Logistic regression -- Decision trees -- Neural networks -- Deep learning -- Clustering -- Natural language processing -- Network analysis -- Recommender systems -- Databases and SQL -- MapReduce -- Data ethics -- Go forth and do data science.

Konu Terimleri:

Python (Bilgisayar program dili)

Veri tabanı yönetimi.

Veri yapıları (Bilgisayar bilimi)

Veri madenciliği.

Veri madenciliği -- Matematik.

Python (Computer program language)

Database management.

Data structures (Computer science)

Data mining.

Data mining -- Mathematics.

Mevcut:*

Library	Materyal Türü	Barkod	Yer Numarası	Durum
Searching... Pamukkale Merkez Kütüphanesi	Kitap	0107490	QA76.73 G78 2019	Searching... Unknown

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Özet

To really learn data science, you should not only master the tools--data science libraries, frameworks, modules, and toolkits--but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data.

Get a crash course in Python Learn the basics of linear algebra, statistics, and probability--and how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Author Notes

Joel Grus is a research engineer at the Allen Institute for Artificial Intelligence. Previously he worked as a software engineer at Google and a data scientist at several startups. He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com and tweets all day long at @joelgrus.