This book aims at providing fundamental concepts of Python programming. It is a good text book as well as reference book. Here concepts are presented in the form of programs making it quite easy and simple for students to understand. It showcases actual screen shots of the programs from the programming environment to make it more student friendly. Because of the user friendly interface provided in the book a novice learner can also learn Python programming without any difficulty. As Python is open source, programs written in this book can execute on different operating systems like Windows, Linux, and Mac etc. Topics covered in this book Python Language Basics, Python Collections- Compound Data Types, List, Tuple, Dictionary and Set, Setting up Development Environment, Introduction to Anaconda and Installation of Anaconda. All the concepts are explained with practical examples and screenshots of the same are embedded.

Have you been seriously thinking about digging into programming but don't know where to start? Are you looking for a quick boost to your career growth? In this Python programming crash course, you will be guided by a quick and thorough introduction intended solely for beginners who want to understand Python programming and learn how to write helpful programs. The book is aimed at getting you fast enough to accelerate and get you to write real programs in no moment. This book is also designed for programmers who have a vague language understanding and would like to brush up their knowledge before trying to program their Python hands-on. The aim of this ultimate guide is to keep each section's thoughts and provide step-by-step guidance to make the learning experience smooth and gradual.It will also address how any future frustration can be reduced. Each code unit is tested, executed and re-read closely. In addition, the INTERACTIVE exercises are optimized for the highest level of commitment, meaning you're not going to get bored to death. Here is what you will find in this book on Pythons for Beginners: A History of Python and the basic concepts of Python Programming How to prepare your computer for programming in Python and how to install Python on Windows, Mac, and Linux. Screenshots included. Python functions that you'll use often. How to work with various data types including strings, lists, tuples, dictionaries, booleans, and many more. How to begin creating the Command Line Search Tool and make programs with Python Sockets And much more... After reading this book, you will realize that Python Programming is not difficult at all and you don't need to be rocket scientist to learn it.This revised and thoroughly tested Python guide will get you up to speed and quickly get you to write true programs. So, why wait any longer? Click the buy button now and start your code and motors!

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. 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. What You Will LearnDiscover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

This book presents selected contributions to the 16th International Conference on Global Research and Education Inter-Academia 2017 hosted by Alexandru Ioan Cuza University of Iași, Romania from 25 to 28 September 2017. It is the third volume in the series, following the editions from 2015 and 2016. Fundamental and applied research in natural sciences have led to crucial developments in the ongoing 4th global industrial revolution, in the course of which information technology has become deeply embedded in industrial management, research and innovation – and just as deeply in education and everyday life. Materials science and nanotechnology, plasma and solid state physics, photonics, electrical and electronic engineering, robotics and metrology, signal processing, e-learning, intelligent and soft computing have long since been central research priorities for the Inter-Academia Community (I-AC) – a body comprising 14 universities and research institutes from Japan and Central/East-European countries that agreed, in 2002, to coordinate their research and education programs so as to better address today’s challenges. The book is intended for use in academic, government, and industrial R&D departments as a reference tool in research and technology education. The 42 peer-reviewed papers were written by more than 119 leading scientists from 14 countries, most of them affiliated to the I-AC.

This book constitutes the refereed proceedings of the 13th International Conference on Brain Informatics, BI 2020, held in Padua, Italy, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 33 full papers were carefully reviewed and selected from 57 submissions. The papers are organized in the following topical sections: cognitive and computational foundations of brain science; investigations of human information processing systems; brain big data analytics, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain-inspired computing.

This book constitutes the refereed proceedings of the 5th International Conference on Advances in Visual Informatics, IVIC 2017, held in Bangi, Malaysia, in November 2017. The keynote and 72 papers presented were carefully reviewed and selected from 130 submissions. The papers are organized in the following topics: Visualization and Data Driven Technology; Engineering and Data Driven Innovation; Data Driven Societal Well-being and Applications; and Data Driven Cyber Security.