Mastering Numerical Computing with NumPy
Language: English
Published by Packt Publishing, 2018
- Softcover
- New
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- Title
- Mastering Numerical Computing with NumPy
- Author
- Cakmak, Umit Mert; Cuhadaroglu, Mert
- Publisher
- Packt Publishing
- Publication year
- 2018
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1788993357
- ISBN 13
- 9781788993357
Enhance the power of NumPy and start boosting your scientific computing capabilities
Key Features
- Grasp all aspects of numerical computing and understand NumPy
- Explore examples to learn exploratory data analysis (EDA), regression, and clustering
- Access NumPy libraries and use performance benchmarking to select the right tool
Book Description
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.
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
Who This Book Is For
Mastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book.
Table of Contents
- Working with NumPy Arrays
- Linear Algebra with NumPy
- Explanatory Data Analysis of US Housing Data withNumPy Statistics
- Predicting Housing Prices Using Linear Regression
- Clustering Clients of Wholesale Distributor Using NumPy
- Python ML Squad: NumPy, SciPy, Pandas, Scikit-Learn
- Advanced Numpy
- Overview of High-Performance Numerical Computing Libraries
- Performance Benchmarks
"Synopsis" may belong to another edition of this title.
About the Author
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 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.
"About the title" may belong to another edition of this title.
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