Mastering Numerical Computing with NumPy
Master scientific computing and perform complex operations with ease
Published on 23. September 2024
248 pages
978-1-78899-684-6 (ISBN)
Description
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 DescriptionNumPy 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 forMastering 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.
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 DescriptionNumPy 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 forMastering 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.
More details
Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
14,54 MB
ISBN-13
978-1-78899-684-6 (9781788996846)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Umit Mert Cakmak | Mert Cuhadaroglu
Mastering Numerical Computing with NumPy
Book
06/2018
Packt Publishing
€45.70
Shipment within 3-4 weeks
Persons
Mert Cakmak Umit :
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.Antao Tiago :
Tiago Antao is a bioinformatician currently working in the field of genomics. A former computer scientist, Tiago moved into computational biology with an MSc in Bioinformatics from the Faculty of Sciences at the University of Porto (Portugal) and a PhD on the spread of drug-resistant malaria from the Liverpool School of Tropical Medicine (UK). Postdoctoral, Tiago has worked with human datasets at the University of Cambridge (UK) and with mosquito whole genome sequencing data at the University of Oxford (UK), before helping to set up the bioinformatics infrastructure at the University of Montana. He currently works as a data engineer in the biotechnology field in Boston, MA. He is one of the co-authors of Biopython, a major bioinformatics package written in Python.Cuhadaroglu Mert :
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.
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.Antao Tiago :
Tiago Antao is a bioinformatician currently working in the field of genomics. A former computer scientist, Tiago moved into computational biology with an MSc in Bioinformatics from the Faculty of Sciences at the University of Porto (Portugal) and a PhD on the spread of drug-resistant malaria from the Liverpool School of Tropical Medicine (UK). Postdoctoral, Tiago has worked with human datasets at the University of Cambridge (UK) and with mosquito whole genome sequencing data at the University of Oxford (UK), before helping to set up the bioinformatics infrastructure at the University of Montana. He currently works as a data engineer in the biotechnology field in Boston, MA. He is one of the co-authors of Biopython, a major bioinformatics package written in Python.Cuhadaroglu Mert :
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.
Content
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
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
