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Begin to code with Python / Rob Miles
[Place of publication not identified] : Pearson Education, 2018
892 pages. : illustrations ; cm.
Ký hiệu phân loại (DDC): 005.133
Start writing software that solves real problems, even if you have absolutely no programming experience! This friendly, easy, full-color book puts you in total control of your own learning, empowering you to build unique and useful programs. Microsoft has completely reinvented the beginning programmer’s tutorial, reflecting deep research into how today’s beginners learn, and why other books fall short. Begin to Code with Python is packed with innovations, from its “Snaps” prebuilt operations to its “Make Something Happen” projects. Whether you’re a total beginner or you’ve tried before, this guide will put the power, excitement, and fun of programming where it belongs: in your hands!
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Machine learning applications using Python : cases studies from healthcare, retail, and finance / Puneet Mathur
[New York, New York] : Apress, 2019
384 pages. : illustrations ; cm.
Ký hiệu phân loại (DDC): 006.3
Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning.
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Numerical Python : scientific computing and data science applications with Numpy, SciPy and Matplotlib / Robert Johansson
[Berkeley, CA] : Apress, 2019
709 pages. : illustrations ; cm.
Ký hiệu phân loại (DDC): 005.133
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python for rapid development and exploratory computing due to its simple and high-level syntax and multiple options for data analysis. After reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning
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