Take a look at our Python books. Shulph carries a great selection of Python books, and we are always adding more.
Mastering Advanced Python Programming Key Features - In-depth coverage on fundamentals of functions, recursion, classes, inheritance, and files. - Mastery of advanced topics - Database connectivity, Errors and Exception, Testing and Debugging, threads, Data visualization, and Data analysis. - In-depth coverage of advanced concepts such as data structures, and algorithms. - Simplifies GUI and Widgets. Description Advance Core Python Programming is designed for Programmers who have a good understanding of Python basics and are ready to take the next steps. For entry-level Python programmers willing to dive deeper into programming, this book provides a path that will help them to add innovative features to their applications. This book starts by introducing you to the concept of Functions and Recursion and then moves on to higher levels of introducing you to OOP concepts, Files, integrating Python with database, threading, errors, exceptions, testing, debugging, data visualization, data analysis, GUI, data structures and algorithms. All these topics are the need of the hour and this book simplifies all these critical and essential concepts of Python for you. Knowledge of these topics will ease the functioning of your envisioned application. Throughout the book, you will have access to several coding examples which will help you to understand the real practical application of advanced Python concepts and you will be able to work on any kind of Python project with confidence. What you will learn - Learn the essentials of Sentiment Analysis. - Learn the essentials of Emotion Analysis. - Conducting Emotion Analysis using Lexicons. - Learn to develop Shiny applications. Who this book is for This book is highly appealing to all tech-savvy students, programming enthusiasts, IT graduates, and computer science professionals who want to build strong proficiency in building Python applications. Prior understanding of Python basic coding concepts like variables, expressions, and control structures is required to begin with this book. You can also read Basic Core Python Programming to develop strong fundamentals before you start with this book. Table of Contents 1. Functions and Recursion 2. Classes, Objects, and Inheritance 3. Files 4. MySQL for Python 5. Python Threads 6. Errors, Exceptions, Testing, and Debugging 7. Data Visualization and Data Analysis 8. Creating the GUI form and Adding Widgets 9. MySQL and Python Graphical User Interface 10. Stack, Queue, and Deque 11. Linked List 12. Trees 13. Searching and Sorting 14. Getting Started with Flask About the Authors Meenu Kohli, author of “Python Interview Questions” and “Basic Core Python Programming” is presenting her next venture – “Advance Core Python Programming”. A BE (Electronics) from D.Y. Patil College of Engineering, Pune University, she has worked extensively as a Software Developer, Tester, and Trainer in reputed MNCs. She has experience of working on challenging projects across varied development environments such as Python, Java, EJB, C, C++, PHP, JSP, JavaScript, HTML, .NET, R, MySQL, Oracle, DB2. She is a keen observer and passionate learner. In addition to software development, she is also a professionally trained software tester and a certified Six Sigma Green Belt from the National Institute of Industrial Engineering – Mumbai. She has experience of teaching Electronics and Computer Science undergraduate students and has also imparted her knowledge to several software-development courses. She later diversified into writing technical books primarily related to Python. LinkedIn Profile: https://www.linkedin.com/in/meenukohli78/
Create distributed applications with clever design patterns to solve complex problems Key Features Set up and run distributed algorithms on a cluster using Dask and PySpark Master skills to accurately implement concurrency in your code Gain practical experience of Python design patterns with real-world examples Book Description This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: Python High Performance - Second Edition by Gabriele Lanaro Mastering Concurrency in Python by Quan Nguyen Mastering Python Design Patterns by Sakis Kasampalis What you will learn Use NumPy and pandas to import and manipulate datasets Achieve native performance with Cython and Numba Write asynchronous code using asyncio and RxPy Design highly scalable programs with application scaffolding Explore abstract methods to maintain data consistency Clone objects using the prototype pattern Use the adapter pattern to make incompatible interfaces compatible Employ the strategy pattern to dynamically choose an algorithm Who this book is for This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.
step-by-step approach to Python programming with machine learning fundamental and theoretical principles. Key Features - Introduces readers to Python programming in a very simple way. - Extensive practical demonstration of Python concepts using numerous examples. - Implementation of machine learning in Python using hands-on techniques. Description The book ‘Introduction to Python Programming: A Practical Approach’ lays out a path for readers who want to pursue a career in the field of computer software development. It covers the fundamentals of Python programming as well as machine learning principles. Students will benefit from the examples that are included with each concept, which will aid them in understanding the concept. This book provides a practical understanding of Python programming using numerous programs and examples. It also develops problem-solving and code-writing abilities for the readers. This book covers Python fundamentals, operators, and data structures such as strings, lists, dictionaries, and tuples. It also contains information on file and exception handling. The implementation of a machine learning model has also been included in this book. With the help of this book, students and programmers can improve their programming skills as well as their ability to sprint towards a rewarding career. What you will learn - Learn Python concepts, operators, and data structures. - Learn the properties and operations of lists, tuples, and dictionaries. - Write Python code to solve specific issues. - Write Python code to handle disk files and exceptions. - Work with OOPS properties like classes, objects, constructors, inheritance, and polymorphism. Who this book is for This book is intended for current and aspiring emerging technology professionals, students, and anyone else who wishes to better understand the Python programming language and machine learning concepts. Table of Contents 1. Chapter 1: Basics of Python Programming 2. Chapter 2: Operators and Expressions 3. Chapter 3: Control Flow Statements 4. Chapter 4: Functions 5. Chapter 5: Strings 6. Chapter 6: Lists 7. Chapter 7: Tuple 8. Chapter 8: Dictionaries 9. Chapter 9: File Handling 10. Chapter 10: Exception Handling, Modules, and Packages 11. Chapter 11: Object-oriented Programming 12. Chapter 12: Machine Learning with Python 13. Chapter 13: Clustering with Python About the Authors Dr. Krishna Kumar Mohbey is an assistant professor of Computer Science at the Central University of Rajasthan, India. He completed his Ph.D. from the Department of Mathematics and Computer Applications from the National Institute of Technology Bhopal, India (2015). His areas of interest are machine learning, data mining, mobile web services, big data analysis, and user behavior analysis. He has authored three books on different subjects and published more than 25 research articles in reputed journals and conferences. LinkedIn Profile: https://in.linkedin.com/in/dr-k-k-mohbey-78947448 Dr. Brijesh Bakariya is working as an assistant professor for the Department of Computer Science and Engineering, I.K. Gujral Punjab Technical University (IKGPTU) Jalandhar (Punjab). He completed his Ph.D. degree from Maulana Azad National Institute of Technology (NIT- Bhopal), Madhya Pradesh (2016). He has authored 01 books and published more than 15 research papers in the journals of international repute in areas of data mining, image processing, machine learning, etc. LinkedIn Profile: https://www.linkedin.com/in/brijesh-bakariya-72b0237a
Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learn Get up and running with the Jupyter ecosystem Identify potential areas of investigation and perform exploratory data analysis Plan a machine learning classification strategy and train classification models Use validation curves and dimensionality reduction to tune and enhance your models Scrape tabular data from web pages and transform it into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.
Document the architecture of your software easily with this highly practical, open-source template. Key Features Get to grips with leveraging the features of arc42 to create insightful documents Learn the concepts of software architecture documentation through real-world examples Discover techniques to create compact, helpful, and easy-to-read documentation Book Description When developers document the architecture of their systems, they often invent their own specific ways of articulating structures, designs, concepts, and decisions. What they need is a template that enables simple and efficient software architecture documentation. arc42 by Example shows how it's done through several real-world examples. Each example in the book, whether it is a chess engine, a huge CRM system, or a cool web system, starts with a brief description of the problem domain and the quality requirements. Then, you'll discover the system context with all the external interfaces. You'll dive into an overview of the solution strategy to implement the building blocks and runtime scenarios. The later chapters also explain various cross-cutting concerns and how they affect other aspects of a program. What you will learn Utilize arc42 to document a system's physical infrastructure Learn how to identify a system's scope and boundaries Break a system down into building blocks and illustrate the relationships between them Discover how to describe the runtime behavior of a system Know how to document design decisions and their reasons Explore the risks and technical debt of your system Who this book is for This book is for software developers and solutions architects who are looking for an easy, open-source tool to document their systems. It is a useful reference for those who are already using arc42. If you are new to arc42, this book is a great learning resource. For those of you who want to write better technical documentation will benefit from the general concepts covered in this book.
Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT dataKey FeaturesDiscover quick solutions to common problems that you'll face while building smart IoT applicationsImplement advanced techniques such as computer vision, NLP, and embedded machine learningBuild, maintain, and deploy machine learning systems to extract key insights from IoT dataBook DescriptionArtificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users' lives easier. With this AI cookbook, you'll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You'll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you'll learn how to deploy models and improve their performance with ease. By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems.What you will learnExplore various AI techniques to build smart IoT solutions from scratchUse machine learning and deep learning techniques to build smart voice recognition and facial detection systemsGain insights into IoT data using algorithms and implement them in projectsPerform anomaly detection for time series data and other types of IoT dataImplement embedded systems learning techniques for machine learning on small devicesApply pre-trained machine learning models to an edge deviceDeploy machine learning models to web apps and mobile using TensorFlow.js and JavaWho this book is forIf you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. Knowledge of the Python programming language and basic IoT concepts is required to grasp the concepts covered in this artificial intelligence book more effectively.
Create better and easy-to-use deep learning models with AutoKerasKey FeaturesDesign and implement your own custom machine learning models using the features of AutoKerasLearn how to use AutoKeras for techniques such as classification, regression, and sentiment analysisGet familiar with advanced concepts as multi-modal, multi-task, and search space customizationBook DescriptionAutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions. By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company.What you will learnSet up a deep learning workstation with TensorFlow and AutoKerasAutomate a machine learning pipeline with AutoKerasCreate and implement image and text classifiers and regressors using AutoKerasUse AutoKeras to perform sentiment analysis of a text, classifying it as negative or positiveLeverage AutoKeras to classify documents by topicsMake the most of AutoKeras by using its most powerful extensionsWho this book is forThis book is for machine learning and deep learning enthusiasts who want to apply automated ML techniques to their projects. Prior basic knowledge of Python programming and machine learning is expected to get the most out of this book.
Learn a Programmer-Friendly language Key Features Strengthens the foundations, as a detailed explanation of programming language concepts are given in a simple manner Lists down all the important points that you need to know related to various topics in an organized manner Prepares you for coding related interview and theoretical questions Provides an in-depth explanation of complex topics and Questions It focuses on how to think logically to solve a problem Follows a systematic approach that will help you to prepare for an interview in a short duration of time Exercises are exceptionally useful to complete the reader’s understanding of a topic Description Book will come as a relief to the students wishing to go through a comprehensive work explaining the programming concepts of Python. Examples are given with proper description, offering a variety of practical examples and conceptual problems along with their systematically worked out solutions. It also covers all the concepts which are helpful for the students and beginners to learn the basics of Python programming. What will you learn This book is written, taking into consideration the skills required at the beginner level for understanding Python Programming Concepts. The book covers the practical examples of Python in an easy way so that students can able to understand efficiently. Who this book is for Book promises to be a perfect starting point for beginners and an asset for those having insight towards programming. Table of Contents 1. Introduction 2. Conditions and Loops 3. Arrays and Functions 4. Lists, Tuples, Iterators and GeneratorsDictionaries and Modules 5. File Handling and Databases
Enhance your data analysis and predictive modeling skills using popular Python tools Key Features Cover all fundamental libraries for operation and manipulation of Python for data analysis Implement real-world datasets to perform predictive analytics with Python Access modern data analysis techniques and detailed code with scikit-learn and SciPy Book Description Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python's most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python. What you will learn Explore important Python libraries and learn to install Anaconda distribution Understand the basics of NumPy Produce informative and useful visualizations for analyzing data Perform common statistical calculations Build predictive models and understand the principles of predictive analytics Who this book is for Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book
Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python. Key Features Get a hands-on, fast-paced introduction to the Python data science stack Explore ways to create useful metrics and statistics from large datasets Create detailed analysis reports with real-world data Book Description Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs. What you will learn Use Python to read and transform data into different formats Generate basic statistics and metrics using data on disk Work with computing tasks distributed over a cluster Convert data from various sources into storage or querying formats Prepare data for statistical analysis, visualization, and machine learning Present data in the form of effective visuals Who this book is for Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.