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Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 520
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Book Description
Book 1: Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Project 1, you will learn how to use Scikit-Learn, NumPy, Pandas, Seaborn, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset provided by Kaggle. This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. In Project 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset provided by Kaggle. Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. Book 2: Step by Step Tutorials For Data Science With Python GUI: Traffic And Heart Attack Analysis And Prediction In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset provided by Kaggle. This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. In Chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset provided by Kaggle. Book 3: BRAIN TUMOR: Analysis, Classification, and Detection Using Machine Learning and Deep Learning with Python GUI In this project, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. This dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). It also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. The deep learning models used in this project are MobileNet and ResNet50. In this project, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 520
Get Book Here
Book Description
Book 1: Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Project 1, you will learn how to use Scikit-Learn, NumPy, Pandas, Seaborn, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset provided by Kaggle. This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine. In Project 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict breast cancer using Breast Cancer Prediction Dataset provided by Kaggle. Worldwide, breast cancer is the most common type of cancer in women and the second highest in terms of mortality rates.Diagnosis of breast cancer is performed when an abnormal lump is found (from self-examination or x-ray) or a tiny speck of calcium is seen (on an x-ray). After a suspicious lump is found, the doctor will conduct a diagnosis to determine whether it is cancerous and, if so, whether it has spread to other parts of the body. This breast cancer dataset was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. You will develop a GUI using PyQt5 to plot distribution of features, pairwise relationship, test scores, prediced values versus true values, confusion matrix, and decision boundary. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. Book 2: Step by Step Tutorials For Data Science With Python GUI: Traffic And Heart Attack Analysis And Prediction In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In Chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset provided by Kaggle. This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. In Chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset provided by Kaggle. Book 3: BRAIN TUMOR: Analysis, Classification, and Detection Using Machine Learning and Deep Learning with Python GUI In this project, you will learn how to use Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Seaborn, and other libraries to implement brain tumor classification and detection with machine learning using Brain Tumor dataset provided by Kaggle. This dataset contains five first order features: Mean (the contribution of individual pixel intensity for the entire image), Variance (used to find how each pixel varies from the neighboring pixel 0, Standard Deviation (the deviation of measured Values or the data from its mean), Skewness (measures of symmetry), and Kurtosis (describes the peak of e.g. a frequency distribution). It also contains eight second order features: Contrast, Energy, ASM (Angular second moment), Entropy, Homogeneity, Dissimilarity, Correlation, and Coarseness. The machine learning models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, and Support Vector Machine. The deep learning models used in this project are MobileNet and ResNet50. In this project, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, training loss, and training accuracy.
Author: Hamzan Wadi
Publisher: TURIDA Publisher
ISBN:
Category : Computers
Languages : en
Pages : 384
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Book Description
This book provides a practical explanation of database programming using Python GUI & MySQL. The discussion in this book is presented in step by step so that it will help readers understand each material and also will make it easier for the readers to follow all of the instructions. This book is very suitable for students, programmers, and anyone who want to learn database programming using Python GUI & MySQL from scratch. This book is divided into two parts: The first part of this book will discuss about the fundamentals of database programming using Python GUI & MySQL. This part will discuss in detail about how to setup your working environment and how to understand GUI programming using Python. This part will also discuss in detail about how to start your database programming using Python GUI & MySQL. This part will discuss in detail about the basic of database programming using Python GUI & MySQL. The second part of this book will discuss about how to build database application using Python GUI & MySQL. This part will discuss in detail about how to build Multiple Document Interface (MDI) database application through real project-based example. This part will discuss in detail about how to design and create database for Library Management System application, and how to create all forms for the application. The final objective of this book is that the readers are able to create real database application using Python GUI & MySQL. Here are the materials that you will learn in this book. PART I: THE FUNDAMENTAL OF DATABASE PROGRAMMING USING PYTHON GUI & MySQL CHAPTER 1: The discussion in this chapter will guide you in preparing what software are needed to start your database programming using Python GUI. This chapter will guide you to install all software including Python, MySQL, and Qt Designer. In addition, this chapter also will discuss about how to understand and use Qt Designer for user interface design, and how to create a GUI application using Python and Qt Designer. CHAPTER 2: The discussion in this chapter will guide you to start your database programming using Python GUI & MySQL. This chapter will discuss in detail about the basic of database programming using Python GUI & MySQL. The discussion in this chapter will talk about how to create and drop database, how to create and drop table, how to insert data into table, how to display data from table, how to update data in table, and how to delete data in table. All discussions in this chapter will give you deep understanding of database programming using Python GUI & MySQL. PART II: BUILDING DATABASE APPLICATION USING PYTHON GUI & MySQL, CASE STUDY: LIBRARY MANAGEMENT SYSTEM APPLICATION CHAPTER 3: The discussion in this chapter will guide you to design and create database for library management system application. This is the first step that must be taken to create database application using Python GUI & MySQL. This chapter will discuss in detail about how to design the Entity Relationship Diagram (ERD) for library management system application. The discussion in this chapter will also talk about how to create database and its tables based on the ERD design using MySQL server. CHAPTER 4: The discussion in this chapter will guide you to create main form and login form for the application. This chapter will discuss in detail about how to create these two forms. These forms are the first two forms that we will create in building library management system application. This chapter will also discuss about how to run the application. CHAPTER 5: The discussion in this chapter will guide you to create user accounts form and members form for Library Management System application. This chapter will discuss in detail about how to create these two forms. This chapter will also discuss about how to add these two forms as MDI sub windows of the main form. And the final discussion of this chapter will guide you to use the forms to manage user accounts and members data of Library Management System application. CHAPTER 6: The discussion in this chapter will guide you to create authors form, genres form, and books form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add books form as MDI sub window of the main form. And the final discussion of this chapter will guide you to use the forms to manage authors, genres, and books data in Library Management System application. CHAPTER 7: The discussion in this chapter will guide you to create member search form, book search form, and loan transaction form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add loan transaction form as MDI sub window of the main form. And the final discussion of this chapter will guide you to use the forms to manage loan transactions in Library Management System application. CHAPTER 8: The discussion in this chapter will guide you to create members statistic form, books statistic form, and loan statistic form for Library Management System application. This chapter will discuss in detail about how to create these three forms. This chapter will also discuss about how to add all of the forms as MDI sub windows of the main form. And the final discussion of this chapter will guide you to use all of the forms to display the statistics in the library.
Author: Hamzan Wadi
Publisher: TR Publisher
ISBN:
Category : Computers
Languages : en
Pages : 340
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Book Description
This book comes as an answer for students, lecturers, or the general public who want to learn Java GUI programming starting from scratch. This book is suitable for beginner learners who want to learn Java GUI programming from the basic to the database level. This book is also present for JAVA learners who want to increase their level of making GUI-based database applications for small, medium, or corporate businesses level. The discussion in this book is not wordy and not theoretical. Each discussion in this book is presented in a concise and clear brief, and directly to the example that implements the discussion. Beginner learners who want to learn through this book should not be afraid of losing understanding of the programming concepts, because this book in detail discusses the concepts of Java programming from the basic to the advanced level. By applying the concept of learning by doing, this book will guide you step by step to start Java GUI programming from the basics until you are able to create database applications using JDBC and MySQL. Here are the material that you will learn in this book. CHAPTER 1 : This chapter will give you brief and clear introduction about how to create desktop application using Java GUI starting from how to setup your environments, create your first project, understand various control for your form, and understand how to interact with your form using event handling. CHAPTER 2 : This chapter will discuss clearly about the concept and the implementatiton of data types and variables in Java GUI. CHAPTER 3 : This chapter will discuss in detail about how to make decisions or deal with a condition in the program. This chapter is the first step to deeper understanding of logics in programming. This chapter specifically discusses relational operators and logical operators, if statements, if-else statements, and switch-case statements, and how to implement all of these conditional statements using Java GUI. CHAPTER 4 : This chapter will discuss in detail the looping statements in Java including for statement, while statement, do-while statement, break statement, and continue statement. All of these looping statements will be implemented using Java GUI. CHAPTER 5 : This chapter will discuss how to use methods to group codes based on their funcitonality. This discussion will also be the first step for programmers to learn how to create efficient program code. This chapter will discuss in detail the basics of methods, methods with return values, how to pass parameters to methods, how to overload your methods, and how to make recursive methods. CHAPTER 6 : This chapter will discuss in detail how to create and use arrays, read and write file operations, and how to display data stored in arrays or files in graphical form. CHAPTER 7 : This chapter will discuss in detail the basics of MySQL, how to access databases using JDBC and MySQL, and how to perform CRUD operations using JDBC and MySQL. CHAPTER 8 : In this chapter we will discuss more about Java GUI programming. This chapter will discuss in detail about how to make a program that consists of multi forms, how to create MDI application, and how to create report using iReport with data stored in a database.
Author: Kohei Arai
Publisher: Springer Nature
ISBN: 303098012X
Category : Technology & Engineering
Languages : en
Pages : 952
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Book Description
The book “Advances in Information and Communication Networks - Proceedings of the 2022 Future of Information and Communication Conference (FICC)” aims in presenting the latest research advances, sharing expert knowledge and exchanging ideas with the common goal of shaping the future of Information and Communication. The conference attracted 402 submissions, of which, 131 submissions (including six poster papers) have been selected through a double-blind review process by an international panel of expert referees. This book discusses on aspects of Communication, Data Science, Ambient Intelligence, Networking, Computing, Security and Internet of Things, from classical to intelligent scope. The intention is to help academic pioneering researchers, scientists, industrial engineers, and students become familiar with and stay abreast of the ever-changing technology surrounding their industry. We hope that readers find the volume interesting and valuable; it gathers chapters addressing state-of-the-art intelligent methods and techniques for solving real world problems along with a vision of the future research.
Author: Rick J. Scavetta
Publisher: "O'Reilly Media, Inc."
ISBN: 1492093378
Category : Computers
Languages : en
Pages : 199
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Book Description
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together
Author: Saurabh Chandrakar
Publisher: BPB Publications
ISBN: 9365895669
Category : Computers
Languages : en
Pages : 745
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Book Description
DESCRIPTION In today's data-driven world, the ability to extract meaningful insights from vast datasets is crucial for success in various fields. This ultimate book for mastering open-source libraries of data science in Python equips you with the essential tools and techniques to navigate the ever-evolving field of data analysis and visualization. Discover how to use Python libraries like NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. This book also covers scientific computing with SciPy and integrates ChatGPT to boost your data science workflow. Designed for data scientists, analysts, and beginners, it offers a practical, hands-on approach to mastering data science fundamentals. With real-world applications and exercises, you will turn raw data into actionable insights, gaining a competitive edge. This book covers everything you need, including open-source libraries, Visual Explorer tools, and ChatGPT, making it a one-stop resource for Python-based data science. Readers will gain confidence after going through this book and we assure you that all the minute details have been taken into consideration while delivering the content. After reading, learning, and practicing from this book, we are sure that all IT professionals, novices, or job seekers will be able to work on data science projects thus proving their mettle. KEY FEATURES ● Master key Python libraries like NumPy, Pandas, and Seaborn for effective data analysis and visualization. ● Understand complex data science concepts through simple explanations and practical examples. ● Get hands-on experience with 300+ solved examples to solidify your Python data science skills. WHAT YOU WILL LEARN ● Learn to work with popular IDEs like VS Code and Jupyter Notebook for efficient Python development. ● Master open-source libraries such as NumPy, SciPy, Matplotlib, and Pandas through advanced, real-world examples. ● Utilize automated EDA tools like PyGWalker and AutoViz to simplify complex data analysis. ● Create sophisticated visualizations like heatmaps, FacetGrid, and box plots using Matplotlib and Seaborn. ● Efficiently handle missing data, outliers, and perform filtering, sorting, grouping, and aggregation using Pandas and Polars. WHO THIS BOOK IS FOR This book is ideal for diploma, undergraduate, and postgraduate students from engineering and science fields to programming and software professionals. It is also perfect for data science, ML, and AI engineers looking to expand their expertise in cutting-edge technologies. TABLE OF CONTENTS 1. Environmental Setup for Using Data Science Libraries in Python 2. Exploring Numpy Library for Data Science in Python 3. Exploring Array Manipulations in Numpy 4. Exploring Scipy Library for Data Science in Python 5. Line Plot exploration with Matplotlib Library 6. Charting Data With Various Visuals Using Matplotlib 7. Exploring Pandas Series for Data Science in Python 8. Exploring Pandas Dataframe for Data Science in Python 9. Advanced Dataframe Filtering Techniques 10. Exploring Polars Library for Data Science in Python 11. Exploring Expressions in Polars 12. Exploring Seaborn Library for Data Science in Python 13. Crafting Seaborn Plots: KDE, Line, Violin and Facets 14. Integrating Data Science Libraries with ChatGPT Prompts 15. Exploring Automated EDA Libraries for Machine Learning 16. Case Study Using Python Data Science Libraries
Author: John V. Guttag
Publisher: MIT Press
ISBN: 0262529629
Category : Computers
Languages : en
Pages : 466
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Book Description
The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.
Author: Mark Summerfield
Publisher: Pearson Education
ISBN: 0132703068
Category : Computers
Languages : en
Pages : 763
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Book Description
Whether you're building GUI prototypes or full-fledged cross-platform GUI applications with native look-and-feel, PyQt 4 is your fastest, easiest, most powerful solution. Qt expert Mark Summerfield has written the definitive best-practice guide to PyQt 4 development. With Rapid GUI Programming with Python and Qt you'll learn how to build efficient GUI applications that run on all major operating systems, including Windows, Mac OS X, Linux, and many versions of Unix, using the same source code for all of them. Summerfield systematically introduces every core GUI development technique: from dialogs and windows to data handling; from events to printing; and more. Through the book's realistic examples you'll discover a completely new PyQt 4-based programming approach, as well as coverage of many new topics, from PyQt 4's rich text engine to advanced model/view and graphics/view programming. Every key concept is illuminated with realistic, downloadable examples–all tested on Windows, Mac OS X, and Linux with Python 2.5, Qt 4.2, and PyQt 4.2, and on Windows and Linux with Qt 4.3 and PyQt 4.3.
Author: Dusty Phillips
Publisher: Packt Publishing Ltd
ISBN: 1786468417
Category : Computers
Languages : en
Pages : 1255
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Book Description
Unleash the power of Python and its robust data science capabilities About This Book Unleash the power of Python 3 objects Learn to use powerful Python libraries for effective data processing and analysis Harness the power of Python to analyze data and create insightful predictive models Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Who This Book Is For Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis. What You Will Learn Install and setup Python Implement objects in Python by creating classes and defining methods Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis Create effective visualizations for presenting your data using Matplotlib Process and analyze data using the time series capabilities of pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply data mining concepts to real-world problems Compute on big data, including real-time data from the Internet Explore how to use different machine learning models to ask different questions of your data In Detail The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Style and approach This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.
Author: Mark Lutz
Publisher: "O'Reilly Media, Inc."
ISBN: 0596554494
Category : Computers
Languages : en
Pages : 749
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Book Description
Portable, powerful, and a breeze to use, Python is ideal for both standalone programs and scripting applications. With this hands-on book, you can master the fundamentals of the core Python language quickly and efficiently, whether you're new to programming or just new to Python. Once you finish, you will know enough about the language to use it in any application domain you choose. Learning Python is based on material from author Mark Lutz's popular training courses, which he's taught over the past decade. Each chapter is a self-contained lesson that helps you thoroughly understand a key component of Python before you continue. Along with plenty of annotated examples, illustrations, and chapter summaries, every chapter also contains Brain Builder, a unique section with practical exercises and review quizzes that let you practice new skills and test your understanding as you go. This book covers: Types and Operations -- Python's major built-in object types in depth: numbers, lists, dictionaries, and more Statements and Syntax -- the code you type to create and process objects in Python, along with Python's general syntax model Functions -- Python's basic procedural tool for structuring and reusing code Modules -- packages of statements, functions, and other tools organized into larger components Classes and OOP -- Python's optional object-oriented programming tool for structuring code for customization and reuse Exceptions and Tools -- exception handling model and statements, plus a look at development tools for writing larger programs Learning Python gives you a deep and complete understanding of the language that will help you comprehend any application-level examples of Python that you later encounter. If you're ready to discover what Google and YouTube see in Python, this book is the best way to get started.
