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Applied Computational Thinking with Python
Use the computational thinking philosophy to solve complex problems by designing appropriate algorithms to produce optimal results across various domainsKey FeaturesDevelop logical reasoning and problem-solving skills that will help you tackle complex problemsExplore core computer science concepts and important computational thinking elements using practical examplesFind out how to identify the best-suited algorithmic solution for your problemBook DescriptionComputational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.What you will learnFind out how to use decomposition to solve problems through visual representationEmploy pattern generalization and abstraction to design solutionsBuild analytical skills required to assess algorithmic solutionsUse computational thinking with Python for statistical analysisUnderstand the input and output needs for designing algorithmic solutionsUse computational thinking to solve data processing problemsIdentify errors in logical processing to refine your solution designApply computational thinking in various domains, such as cryptography, economics, and machine learningWho this book is forThis book is for students, developers, and professionals looking to develop problem-solving skills and tactics involved in writing or debugging software programs and applications. Familiarity with Python programming is required.
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Deep Dive into the Power Platform in the Age of Generative AI
Understand the full potential of Microsoft Power Platform with this comprehensive guide, designed to provide you with the knowledge and tools needed to create intelligent business applications, automate workflows, and drive data-driven insights for business growth. Whether you're a novice or an experienced professional, this book offers a step-by-step approach to mastering the Power Platform. This book comes with an extensive array of essential concepts, architectural patterns and techniques. It will also guide you with practical insights to navigate the Power Platform effortlessly while integrating on Azure. Starting with exploring Power Apps for building enterprise applications, the book delves into Dataverse, Copilot Studio, AI Builder, managing platforms and Application life cycle management. You will then demonstrate testing strategy followed by a detailed examination of Dataverse and intelligent AI-powered Applications. Additionally, you will cover Power pages for external websites and AI-infused solutions. Each section is meticulously structured, offering step-by-step guidance, hands-on exercises, and real-world scenarios to reinforce learning.After reading the book, you will be able to optimize your utilization of the Power Platform for creating effective business solutions.What You Will Learn:Understand the core components and capabilities of Power PlatformExplore how Power Platform integrates with Azure servicesUnderstand the key features and benefits of using Power Platform for business applicationsDiscover best practices for governance to ensure compliance and efficient managementExplore techniques for optimizing the performance of data integration and export processes on AzureWho This Book Is For:Application developers, Enterprise Architects and business decision-makers.
R 1,482.00
R 451.00
Practical Machine Learning for Streaming Data with Python
Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights. You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow.Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.What You'll LearnUnderstand machine learning with streaming data conceptsReview incremental and online learningDevelop models for detecting concept driftExplore techniques for classification, regression, and ensemble learning in streaming data contextsApply best practices for debugging and validating machine learning models in streaming data contextGet introduced to other open-source frameworks for handling streaming data.Who This Book Is ForMachine learning engineers and data science professionals
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Programming with Python
Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency.
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Hands-on Time Series Analysis with Python
Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks.You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn:· Explains basics to advanced concepts of time series· How to design, develop, train, and validate time-series methodologies· What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results· Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.· Univariate and multivariate problem solving using fbprophet. Who This Book Is ForData scientists, data analysts, financial analysts, and stock market researchers
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R 611.00
R 458.00
Foundations of Data Science with Python
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.Key Features: Applies a modern, computational approach to working with dataUses real data sets to conduct statistical tests that address a diverse set of contemporary issuesTeaches the fundamentals of some of the most important tools in the Python data-science stackProvides a basic, but rigorous, introduction to Probability and its application to StatisticsOffers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
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R 611.00
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Python Debugging for AI, Machine Learning, and Cloud Computing
This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior.The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you’ll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You’ll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation. This includes tracing, logging, and analyziing memory dumps using native WinDbg and GDB debuggers. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications.What You Will LearnEmploy a pattern-oriented approach to Python debugging that starts with diagnostics of common software problemsUse tips and tricks to get the most out of popular IDEs, notebooks, and command-line Python debuggingUnderstand Python internals for interfacing with operating systems and external modulesPerform Python memory dump analysis, tracing, and loggingWho This Book Is ForSoftware developers, AI/ML engineers, researchers, data engineers, as well as MLOps and DevOps professionals.
R 1,140.00
R 763.00
Debugging Machine Learning Models with Python
Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world successKey FeaturesLearn how to improve performance of your models and eliminate model biasesStrategically design your machine learning systems to minimize chances of failure in productionDiscover advanced techniques to solve real-world challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDebugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies.By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.What you will learnEnhance data quality and eliminate data flawsEffectively assess and improve the performance of your modelsDevelop and optimize deep learning models with PyTorchMitigate biases to ensure fairnessUnderstand explainability techniques to improve model qualitiesUse test-driven modeling for data processing and modeling improvementExplore techniques to bring reliable models to productionDiscover the benefits of causal and human-in-the-loop modelingWho this book is forThis book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills.
R 1,693.00
Igniting Creativity With Reading Intelligence: The Method That Integrates Art And Technology
This book introduces a practical model for developing creative talent in the digital and AI era.It outlines how art, design, and technology can be taught together through capability-driven learning, presenting clear educational models, theoretical foundations, and real-world projects across film, animation, game design, and digital media. It also highlights the role of AI and digital tools in modern creative processes and demonstrates effective teaching methods based on project work and interdisciplinary collaboration.Written for educators, students, and creative-industry trainers, this book provides actionable frameworks to build innovative learning environments and equip learners for today's technology-infused creative fields. It also serves as a useful textbook for courses in digital media, creative education, design pedagogy, and art-and-technology integration.
R 3,957.00
Foundations of Data Science with Python
Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.Key Features: Applies a modern, computational approach to working with dataUses real data sets to conduct statistical tests that address a diverse set of contemporary issuesTeaches the fundamentals of some of the most important tools in the Python data-science stackProvides a basic, but rigorous, introduction to Probability and its application to StatisticsOffers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material
R 3,993.00
Learning Python with Raspberry Pi
The must-have companion guide to the Raspberry Pi User Guide! Raspberry Pi chose Python as its teaching language of choice to encourage a new generation of programmers to learn how to program. This approachable book serves as an ideal resource for anyone wanting to use Raspberry Pi to learn to program and helps you get started with the Python programming language. Aimed at first-time developers with no prior programming language assumed, this beginner book gets you up and running. Covers variables, loops, and functionsAddresses 3D graphics programmingWalks you through programming MinecraftZeroes in on Python for scripting Learning Python with Raspberry Pi proves itself to be a fantastic introduction to coding.
R 775.00
Applied Geospatial Data Science with Python
Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in PythonThe book includes colored images of important conceptsKey FeaturesLearn how to integrate spatial data and spatial thinking into traditional data science workflowsDevelop a spatial perspective and learn to avoid common pitfalls along the wayGain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expandedBook DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you'll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You'll learn how to read, process, and manipulate spatial data effectively. With data in hand, you'll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you'll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you'll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learnUnderstand the fundamentals needed to work with geospatial dataTransition from tabular to geo-enabled data in your workflowsDevelop an introductory portfolio of spatial data science work using PythonGain hands-on skills with case studies relevant to different industriesDiscover best practices focusing on geospatial data to bring a positive change in your environmentExplore solving use cases, such as traveling salesperson and vehicle routing problemsWho this book is forThis book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You'll need to have a foundational knowledge of Python for data analysis and/or data science.
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Generative AI’s “Creative” Process in the Era of Privatisation of Knowledge
This book investigates how generative AI in the creative sector should be regulated in ways that are both effective and normatively desirable in the current era of datafication and knowledge capitalism. In particular, the book analyses the challenges posed by generative AI at both the input and output phases of the so-called “algorithmic creative process” while, on the one hand, examining how the EU legislator seeks to address these challenges—primarily through the AI Act—and, on the other hand, arguing that the analysis should extend beyond digital law to encompass renewed reflection on intellectual property law itself. In this respect, the book emphasises the need to reaffirm the social function that copyright law is meant to pursue, which is essential both to the effective governance of this phenomenon and to mitigating the risks arising from an uncritical acceptance of the ongoing privatisation of knowledge within the current socio-economic context.
R 5,375.00
R 397.00
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R 458.00
Hands-On Image Processing with Python
Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook DescriptionImage processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.By the end of this book, we will have learned to implement various algorithms for efficient image processing.What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is forThis book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.
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R 489.00
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