Search results for "handbook of computer programming with python"
Items we need to order for you
Programming Google App Engine with Python
This practical guide shows intermediate and advanced web and mobile app developers how to build highly scalable Python applications in the cloud with Google App Engine. The flagship of Google's Cloud Platform, App Engine hosts your app on infrastructure that grows automatically with your traffic, minimizing up-front costs and accommodating unexpected visitors. You'll learn hands-on how to perform common development tasks with App Engine services and development tools, including deployment and maintenance. App Engine's Python support includes a fast Python 2.7 interpreter, the standard library, and a WSGI-based runtime environment. Choose from many popular web application frameworks, including Django and Flask.Get a hands-on introduction to App Engine's tools and features, using an example application Simulate App Engine on your development machine with tools from Google Cloud SDK Structure your app into individually addressable modules, each with its own scaling configuration Exploit the power of the scalable Cloud Datastore, using queries, transactions, and data modeling with the ndb library Use Cloud SQL for standard relational databases with App Engine applications Learn how to deploy, manage, and inspect your application on Google infrastructure
R 1,557.00
R 2,618.42
Computer Graphics Programming
TO COMPUTER GRAPHICS BASED ONGKS Part I gives an introduction to basic concepts of computer graph ics and to the principles and concepts of GKS. The aims of this part are twofold: to provide the beginner with an overview of the terminology and concepts of computer graphics, based on GKS, and to give the computer graphics expert an introduc tion to the GKS standard. In the early chapters of this part, the main areas of computer graphics, the various classes of com puter graphics users, the interfaces of GKS and its underlying design concepts are discussed and important terms are defined. The later chapters give an informal introduction to the main concepts of GKS and their interrelationships: output, attributes, coordinate systems, transformations, input, segments, metafile, state lists, and error handling. This introduction to the GKS framework will prepare the ground for the detailed description of 2D GKS functions in Part III and the 3D extensions to GKS in Part IV. 1 WHAT IS COMPUTER GRAPHICS? 1. 1 Defmition of Computer Graphics The Data Processing Vocabulary of the International Organization for Stan dardization (ISO) [ISO 84] defines Computer Graphics as follows: "Methods and techniques for converting data to and from a graphic display via computer. " This definition refers to three basic components of any computer graphics system - namely "data", "computer", and "display".
R 1,848.14
R 916.00
R 2,137.00
R 580.00
Orange Book of Machine Learning Green Edition
Learn supervised machine learning for tabular data using Python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL for regression, classification, and predictive modelingKey FeaturesExplore, clean, and prepare tabular datasets for machine learning workflowsBuild regression and classification models using modern machine learning toolsImprove predictions with calibration, conformal intervals, and optimization techniquesBook DescriptionMaster the essential tools and techniques for supervised machine learning on tabular data with this practical guide to regression and classification. Through clear explanations, code snippets, and hands-on notebooks, you'll learn how to use Python and leading machine learning libraries, including pandas, scikit-learn, CatBoost, LightGBM, XGBoost, TabPFN, and TabICL, to build predictive models for real-world datasets.The book covers the complete workflow, from data exploration and cleaning to model development, evaluation, and optimization. You'll learn how to perform regression analysis for accurate point predictions and estimate uncertainty using conformal prediction intervals. For classification tasks, you'll explore probabilistic predictions and calibration techniques to improve model reliability. You'll also discover practical approaches to feature engineering, feature selection, and hyperparameter optimization to enhance model performance. In addition, the book introduces tabular foundation models and in-context learning techniques, providing insight into the latest advances in machine learning for structured data.By the end of the book, you'll have the skills and confidence to develop, evaluate, and deploy supervised machine learning models for a wide range of tabular data applications.What you will learnPerform exploratory data analysis and data cleaningApply cross-validation for reliable model evaluationBuild regression models and prediction intervalsDevelop calibrated probabilistic classification modelsOptimize models through hyperparameter tuningEngineer and select features for improved performanceUse ensemble learning methods effectivelyExplore tabular foundation models and in-context learningWho this book is forThis book is designed for motivated self-learners, university students studying applied machine learning, junior data scientists, and academic researchers looking to incorporate machine learning into their analytical workflows. Readers should have a basic familiarity with Python and data analysis concepts. Whether you're developing predictive models for business, research, or educational purposes, this book provides the practical guidance needed to apply modern machine learning techniques to structured and tabular datasets.
R 1,303.00
R 611.00
Hands-On Enterprise Application Development with Python
Architect scalable, reliable, and maintainable applications for enterprises with PythonKey FeaturesExplore various Python design patterns used for enterprise software developmentApply best practices for testing and performance optimization to build stable applicationsLearn about different attacking strategies used on enterprise applications and how to avoid themBook DescriptionDynamically typed languages like Python are continuously improving. With the addition of exciting new features and a wide selection of modern libraries and frameworks, Python has emerged as an ideal language for developing enterprise applications. Hands-On Enterprise Application Development with Python will show you how to build effective applications that are stable, secure, and easily scalable.The book is a detailed guide to building an end-to-end enterprise-grade application in Python. You will learn how to effectively implement Python features and design patterns that will positively impact your application lifecycle. The book also covers advanced concurrency techniques that will help you build a RESTful application with an optimized frontend. Given that security and stability are the foundation for an enterprise application, you’ll be trained on effective testing, performance analysis, and security practices, and understand how to embed them in your codebase during the initial phase. You’ll also be guided in how to move on from a monolithic architecture to one that is service oriented, leveraging microservices and serverless deployment techniques.By the end of the book, you will have become proficient at building efficient enterprise applications in Python.What you will learnUnderstand the purpose of design patterns and their impact on application lifecycleBuild applications that can handle large amounts of data-intensive operationsUncover advanced concurrency techniques and discover how to handle a large number of requests in productionOptimize frontends to improve the client-side experience of your applicationEffective testing and performance profiling techniques to detect issues in applications early in the development cycleBuild applications with a focus on securityImplement large applications as microservices to improve scalabilityWho this book is forIf you’re a developer who wants to build enterprise-grade applications, this book is for you. Basic to intermediate-level of programming experience with Python and database systems is required to understand the concepts covered in this book.
R 1,889.00
R 4,417.00
R 580.00
R 1,496.00
Programming with Higher-Order Logic
Formal systems that describe computations over syntactic structures occur frequently in computer science. Logic programming provides a natural framework for encoding and animating such systems. However, these systems often embody variable binding, a notion that must be treated carefully at a computational level. This book aims to show that a programming language based on a simply typed version of higher-order logic provides an elegant, declarative means for providing such a treatment. Three broad topics are covered in pursuit of this goal. First, a proof-theoretic framework that supports a general view of logic programming is identified. Second, an actual language called λProlog is developed by applying this view to higher-order logic. Finally, a methodology for programming with specifications is exposed by showing how several computations over formal objects such as logical formulas, functional programs, and λ-terms and π-calculus expressions can be encoded in λProlog.
R 2,376.32
Learning Geospatial Analysis with Python
Harness the powerful Python programming language to navigate the realms of geographic information systems, remote sensing, topography, and more, while embracing a guiding framework for effective geospatial analysisKey FeaturesCreate GIS solutions using the new features introduced in Python 3.10Explore a range of GIS tools and libraries, including PostGIS, QGIS, and PROJIdentify the tools and resources that best align with your specific needsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGeospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.What you will learnAutomate geospatial analysis workflows using PythonUnderstand the different formats in which geospatial data is availableUnleash geospatial tech tools to create stunning visualizationsCreate thematic maps with Python tools such as PyShp, OGR, and the Python Imaging LibraryBuild a geospatial Python toolbox for analysis and application developmentUnlock remote sensing secrets, detect changes, and process imageryLeverage ChatGPT for solving Python geospatial solutionsApply geospatial analysis to real-time data tracking and storm chasingWho this book is forThis book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.
R 1,368.00
Artificial Intelligence with Python
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around youAbout This Book• Step into the amazing world of intelligent apps using this comprehensive guide• Enter the world of Artificial Intelligence, explore it, and create your own applications• Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no timeWho This Book Is ForThis book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.What You Will Learn• Realize different classification and regression techniques• Understand the concept of clustering and how to use it to automatically segment data• See how to build an intelligent recommender system• Understand logic programming and how to use it• Build automatic speech recognition systems• Understand the basics of heuristic search and genetic programming• Develop games using Artificial Intelligence• Learn how reinforcement learning works• Discover how to build intelligent applications centered on images, text, and time series data• See how to use deep learning algorithms and build applications based on itIn DetailArtificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications.During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!Style and approachThis highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
R 1,889.00
Practical Graph Intelligence 1
Practical Graph Intelligence 1 is positioned at the intersection of graph theory, network science and applied computing, offering a structured pathway for understanding and implementing graph-based solutions. This book systematically develops core concepts in graph algorithms and network analysis, while emphasizing practical implementation using Python. It explores fundamental structures, traversal techniques, optimization strategies and real-world network modeling, enabling readers to translate theory into scalable applications. Through clear explanations and hands-on examples, the book supports learners in building analytical skills required for domains such as artificial intelligence (AI), data science, cybersecurity and social network analysis. Designed for students, researchers and professionals, this book bridges the gap between mathematical foundations and computational practice, fostering the development of efficient and intelligent network-driven systems.
R 5,536.00
R 1,863.74
Learn Quantum Computing with Python and IBM Quantum Experience
A step-by-step guide to learning the implementation and associated methodologies in quantum computing with the help of the IBM Quantum Experience, Qiskit, and Python that will have you up and running and productive in no timeKey FeaturesDetermine the difference between classical computers and quantum computersUnderstand the quantum computational principles such as superposition and entanglement and how they are leveraged on IBM Quantum Experience systemsRun your own quantum experiments and applications by integrating with QiskitBook DescriptionIBM Quantum Experience is a platform that enables developers to learn the basics of quantum computing by allowing them to run experiments on a quantum computing simulator and a real quantum computer. This book will explain the basic principles of quantum mechanics, the principles involved in quantum computing, and the implementation of quantum algorithms and experiments on IBM's quantum processors. You will start working with simple programs that illustrate quantum computing principles and slowly work your way up to more complex programs and algorithms that leverage quantum computing. As you build on your knowledge, you'll understand the functionality of IBM Quantum Experience and the various resources it offers. Furthermore, you'll not only learn the differences between the various quantum computers but also the various simulators available. Later, you'll explore the basics of quantum computing, quantum volume, and a few basic algorithms, all while optimally using the resources available on IBM Quantum Experience. By the end of this book, you'll learn how to build quantum programs on your own and have gained practical quantum computing skills that you can apply to your business.What you will learnExplore quantum computational principles such as superposition and quantum entanglementBecome familiar with the contents and layout of the IBM Quantum ExperienceUnderstand quantum gates and how they operate on qubitsDiscover the quantum information science kit and its elements such as Terra and AerGet to grips with quantum algorithms such as Bell State, Deutsch-Jozsa, Grover's algorithm, and Shor's algorithmHow to create and visualize a quantum circuitWho this book is forThis book is for Python developers who are looking to learn quantum computing and put their knowledge to use in practical situations with the help of IBM Quantum Experience. Some background in computer science and high-school-level physics and math is required.
R 1,498.00
R 1,010.00
Introduction to Generative AI with Julia and Python
Elevate your coding capabilities and gain the skills necessary for maximized performance and career opportunities in the field of Artificial Intelligence. This book is a step-by-step guide designed to equip software engineers with solid foundation of Generative AI basics. The book begins by introducing Generative AI using Google Colab to establish a solid foundation, before demonstrating how to leverage Python and Julia's AI capabilities to construct Artificial Neural Networks. You'll then explore advanced AI models, such as variational autoencoders, in order to generative images and text. Author Pierluigi Riti not only navigates the complexities of this emerging topic but fills a crucial gap by guiding software engineers through theory before leading them to produce different types of models using only free resources. What You Will Learn Acquire a solid foundation of Generative AI basics.Utilize the capabilities of Python and Julia to constructure Artificial Neural NetworksExplore advanced AI models including variational autoencoders, to generate images Survey different architectures for AI and utilize free resources to produce different AI Who This Book Is For Programmers new to artificial intelligence, but with a fundamental grasp of Python.
R 1,368.00
Applying Math with Python
Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to utilize Python's libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionPython, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.What you will learnGet familiar with basic packages, tools, and libraries in Python for solving mathematical problemsExplore various techniques that will help you to solve computational mathematical problemsUnderstand the core concepts of applied mathematics and how you can apply them in computer scienceDiscover how to choose the most suitable package, tool, or technique to solve a certain problemImplement basic mathematical plotting, change plot styles, and add labels to the plots using MatplotlibGet to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methodsWho this book is forThis book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.
R 1,335.00
R 1,038.00
R 764.00
Developing Graphics Frameworks with Python and OpenGL
Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds.You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results.
R 6,118.00
Internet of Things with Python
Interact with the world and rapidly prototype IoT applications using PythonAbout This Book• Rapidly prototype even complex IoT applications with Python and put them to practical use• Enhance your IoT skills with the most up-to-date applicability in the field of wearable tech, smart environments, and home automation• Interact with hardware, sensors, and actuators and control your DIY IoT projects through PythonWho This Book Is ForThe book is ideal for Python developers who want to explore the tools in the Python ecosystem in order to build their own IoT applications and work on IoT-related projects. It is also a very useful resource for developers with experience in other programming languages that want to easily prototype IoT applications with the Intel Galileo Gen 2 board.What You Will Learn• Prototype and develop IoT solutions from scratch with Python as the programming language• Develop IoT projects with Intel Galileo Gen 2 board along with Python• Work with the different components included in the boards using Python and the MRAA library• Interact with sensors, actuators, and shields• Work with UART and local storage• Interact with any electronic device that supports the I2C bus• Allow mobile devices to interact with the board• Work with real-time IoT and cloud services• Understand Big Data and IoT analyticsIn DetailInternet of Things (IoT) is revolutionizing the way devices/things interact with each other. And when you have IoT with Python on your side, you'll be able to build interactive objects and design them.This book lets you stay at the forefront of cutting-edge research on IoT. We'll open up the possibilities using tools that enable you to interact with the world, such as Intel Galileo Gen 2, sensors, and other hardware. You will learn how to read, write, and convert digital values to generate analog output by programming Pulse Width Modulation (PWM) in Python. You will get familiar with the complex communication system included in the board, so you can interact with any shield, actuator, or sensor.Later on, you will not only see how to work with data received from the sensors, but also perform actions by sending them to a specific shield. You'll be able to connect your IoT device to the entire world, by integrating WiFi, Bluetooth, and Internet settings. With everything ready, you will see how to work in real time on your IoT device using the MQTT protocol in python.By the end of the book, you will be able to develop IoT prototypes with Python, libraries, and tools.Style and approach This book takes a tutorial-like approach with mission critical chapters. The initial chapters are introductions that set the premise for useful examples covered in later chapters.
R 1,889.00
Algebra and Geometry with Python
This book teaches algebra and geometry. The authors dedicate chapters to the key issues of matrices, linear equations, matrix algorithms, vector spaces, lines, planes, second-order curves, and elliptic curves. The text is supported throughout with problems, and the authors have included source code in Python in the book. The book is suitable for advanced undergraduate and graduate students in computer science.
R 2,790.98
R 3,221.74
R 641.00
R 1,221.00
Computer Programming for Kids with Scratch
Are you teaching computer programming to students? This is a quarter-length curriculum to help classroom teachers teach the Scratch programming language. It contains 30 full-color, student-tested tutorials (beginning, intermediate, and advanced), including making games like Pong, Asteroids, Pac-Man, an additional "retro game", an original game, and two real-world challenge programs. Teachers who purchase either the ebook or paperback can make a class set for their students to use. Scratch is a free programming application developed by the MIT Media Lab and is available at http://scratch.mit.edu.
R 967.19
R 550.00
OpenCV Computer Vision Application Programming Cookbook
OpenCV 3 Computer Vision Application Programming Cookbook is appropriate for novice C++ programmers who want to learn how to use the OpenCV library to build computer vision applications. It is also suitable for professional software developers wishing to be introduced to the concepts of computer vision programming. It can also be used as a companion book in a university-level computer vision courses. It constitutes an excellent reference for graduate students and researchers in image processing and computer vision.
R 1,661.00
Programming with TensorFlow
This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).
R 2,602.04
R 611.00
Concepts and Semantics of Programming Languages 2
This book – composed of two volumes – explores the syntactical constructs of the most common programming languages, and sheds a mathematical light on their semantics, providing also an accurate presentation of the material aspects that interfere with coding.Concepts and Semantics of Programming Languages 2 presents an original semantic model, collectively taking into account all of the constructs and operations of modules and classes: visibility, import, export, delayed definitions, parameterization by types and values, extensions, etc. The model serves for the study of Ada and OCaml modules, as well as C header files. It can be deployed to model object and class features, and is thus used to describe Java, C++, OCaml and Python classes.This book is intended not only for computer science students and teachers but also seasoned programmers, who will find a guide to reading reference manuals and the foundations of program verification.
R 5,795.00
Artificial Intelligence with Python
Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence.This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.
R 2,015.00
Hands-On Predictive Analytics with Python
Step-by-step guide to build high performing predictive applications Key FeaturesUse the Python data analytics ecosystem to implement end-to-end predictive analytics projectsExplore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanationsLearn to deploy a predictive model's results as an interactive applicationBook DescriptionPredictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.What you will learnGet to grips with the main concepts and principles of predictive analyticsLearn about the stages involved in producing complete predictive analytics solutionsUnderstand how to define a problem, propose a solution, and prepare a datasetUse visualizations to explore relationships and gain insights into the datasetLearn to build regression and classification models using scikit-learnUse Keras to build powerful neural network models that produce accurate predictionsLearn to serve a model's predictions as a web applicationWho this book is forThis book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.
R 1,661.00
