Mastering Numerical Computing with NumPy: Master scientific computing and perform complex operations with ease

By 作者: Umit Mert Cakmak

ISBN-10 书号: 1788993357

ISBN-13 书号: 9781788993357

Release Finelybook 出版日期: 2018-06-28

pages 页数: 248

$34.99

Book Description to Finelybook sorting

NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts.

Beginning with NumPy’s arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and clustering algorithms, followed by understanding how to write better NumPy code while keeping advanced considerations in mind. The book also demonstrates the use of different high-performance numerical computing libraries and their relationship with NumPy. You will study how to benchmark the performance of different configurations and choose the best for your system.

By the end of this book, you will have become an expert in handling and performing complex data manipulations.

**Contents**

1: WORKING WITH NUMPY ARRAYS

2: LINEAR ALGEBRA WITH NUMPY

3: EXPLORATORY DATA ANALYSIS OF BOSTON HOUSING DATA WITH NUMPY STATISTICS

4: PREDICTING HOUSING PRICES USING LINEAR REGRESSION

5: CLUSTERING CLIENTS OF A WHOLESALE DISTRIBUTOR USING NUMPY

6: NUMPY, SCIPY, PANDAS, AND SCIKIT-LEARN

7: ADVANCED NUMPY

8: OVERVIEW OF HIGH-PERFORMANCE NUMERICAL COMPUTING LIBRARIES

9: PERFORMANCE BENCHMARKS

What You Will Learn

Perform vector and matrix operations using NumPy

Perform exploratory data analysis (EDA) on US housing data

Develop a predictive model using simple and multiple linear regression

Understand unsupervised learning and clustering algorithms with practical use cases

Write better NumPy code and implement the algorithms from scratch

Perform benchmark tests to choose the best configuration for your system

Authors

Umit Mert Cakmak

Umit Mert Cakmak is a data scientist at IBM, where he excels at helping clients solve complex data science problems, from inception to delivery of deployable assets. His research spans multiple disciplines beyond his industry and he likes sharing his insights at conferences, universities, and meet-ups.

Mert Cuhadaroglu

Mert Cuhadaroglu is a BI Developer in EPAM, developing E2E analytics solutions for complex business problems in various industries, mostly investment banking, FMCG, media, communication, and pharma. He consistently uses advanced statistical models and ML algorithms to provide actionable insights. Throughout his career, he has worked in several other industries, such as banking and asset management. He continues his academic research in AI for trading algorithms.

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