DZIEŃ PROGRAMISTY 2026 [Rabaty do -96%]
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As generative AI becomes central to modern data and machine learning workflows, earning the Databricks Certified Generative AI Engineer Associate certification signals both credibility and technical fluency. This study guide prepares you for this sought-after certification with comprehensive coverage of core GenAI and LLM concepts, practical data m-
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Learning AutoML is your practical guide to applying automated machine learning in real-world environments. Whether you're a data scientist, ML engineer, or AI researcher, this book helps you move beyond experimentation to build and deploy high-performing models with less manual tuning and more automation. Using AutoGluon as a primary toolkit, you'l
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The vast potential of AI technology remains untapped in areas like audio, video, and complex data analysis. In fact, many of today's professionals find it challenging to apply AI innovations across these diverse domains due to a lack of guidance and practical implementations.This comprehensive guide, tailored especially for intermediate to advanced
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Navigating the complexities of large-scale spatial data can be daunting. In order to unleash the power of massive and complex datasets, you'll need a cutting-edge tool like Apache Sedona. This innovative distributed computing system, designed specifically for spatial data, has diverse applications in fields such as mobility, telematics, agriculture
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Get up to speed on a new unified approach to building machine learning (ML) systems with a feature store. Using this practical book, data scientists and ML engineers will learn in detail how to develop and operate batch, real-time, and agentic ML systems. Author Jim Dowling introduces fundamental principles and practices for developing, testing, an
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As Snowflake's capabilities expand, staying updated with its latest features and functionalities can be overwhelming. The platform's rapid development gave rise to advanced tools like Snowpark and the Native App Framework, which are crucial for optimizing data operations but may seem complex to navigate. In this essential book, author Muhammad Fasi
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This book provides a beginner-friendly introduction to AI and Machine Learning. Through clear explanations and Python tutorials, it covers deep learning, NLP, and ethical AI, helping readers gain practical knowledge for real-world applications.
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This accessible guide covers Python fundamentals and core machine learning concepts. Ideal for complete beginners looking to break into tech and data science.
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29.90 zł
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AI is transforming industries, and cloud-based AI expertise is in high demand. This study guide is your essential resource for mastering foundational AI concepts and AWS tools while preparing for the AIF-C01 certification exam. Author Tom Taulli breaks down complex topics into practical, easy-to-understand lessons that will enhance the test-readine
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Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-tim
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Eager to learn AI and machine learning but unsure where to start? Laurence Moroney's hands-on, code-first guide demystifies complex AI concepts without relying on advanced mathematics. Designed for programmers, it focuses on practical applications using PyTorch, helping you build real-world models without feeling overwhelmed. From computer vision
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Businesses that want to stay competitive know that AI has become a crucial technology—and so do their employees looking to grow their careers. Earning Microsoft's AI-900: Azure AI Fundamentals certification proves your proficiency with foundational AI concepts. This study guide equips you with the knowledge needed to pass the AI-900 exam, whe
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This book explores the foundations of probability and statistics, guiding readers through key concepts like distributions, hypothesis testing, Bayesian models, and Markov processes. Essential for machine learning.
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A comprehensive introduction to artificial intelligence engines focusing on the mathematics behind neural networks and deep learning, featuring practical examples and hands-on approaches to key AI techniques.
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This book offers a detailed introduction to information theory, covering entropy, coding, and data compression. It explores advanced topics like noisy channels, rate distortion, and the connection between information and thermodynamics.
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Information Theory. A Tutorial Introduction to the Principles and Applications of Information Theory
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Explore the essentials of linear regression using Python, covering topics like finding the best-fitting line, assessing statistical significance, and advanced methods such as Bayesian regression, all supported by Python code examples.
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Master calculus fundamentals essential for AI and machine learning. This guide covers derivatives, integrals, limits, and multivariable calculus, emphasizing applications in neural networks, optimization, and data science techniques.
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Reinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research.This book is among the fir
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Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future
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This course demystifies complex topics, providing you with a solid foundation in data analysis and algorithm optimization. Perfect for technical professionals aiming to enhance their understanding and application of machine learning.
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Learn the core concepts of linear algebra essential for machine learning. This course guides you through vectors, matrices and their applications culminating in principal component analysis, making it a perfect guide for beginners and professionals.
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The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies.If you're a data
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Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitati
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Advance your Power BI skills by adding AI to your repertoire at a practice level. With this practical book, business-oriented software engineers and developers will learn the terminologies, practices, and strategy necessary to successfully incorporate AI into your business intelligence estate. Jen Stirrup, CEO of AI and BI leadership consultancy Da
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Dive into machine learning with our course, designed to guide you in building your own recommender system using Scikit-Learn, complete with practical datasets and ethical considerations.
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Dive into the world of Machine Learning with Python, exploring essential algorithms, data scrubbing techniques, and model design to unlock the potential of AI and data science.
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Approach data science interviews with confidence with this one-stop guide focused on Python programming, SQL, shell/bash, version control, statistics, and ML modeling. No need to scan the internet for technical interview prep material—it's all here!
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Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to app
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Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud API
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The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality record
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Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorit
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Unlocking computer vision with Python and OpenCV KEY FEATURES Practical solutions to image processing challenges. Detect and classify objects in images. Recognize faces and text from images using character detection and recognition models. DESCRIPTION Neural Network Computer Vision with OpenCV equips you with professional skills and knowledg
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Unveiling the power and potential of Generative AI for a limitless future KEY FEATURES Holistic and accessible journey into Generative AI. Indispensable guide for unleashing Generative AI potential. Transforming technology, business, art, and innovation, covering technological advancements and business optimization. DESCRIPTION The Potential
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Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way.In this
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As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tac
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Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In this hands-on guide, author Anth
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This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take u
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Learn how to build a complete machine learning pipeline by mastering feature extraction, feature selection, and algorithm training KEY FEATURES Develop a solid understanding of foundational principles in machine learning. Master regression and classification methods for accurate data prediction and categorization in machine learning. Dive into
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All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools li
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Learn using Cloud data technologies for improving data analytics and decision-making capabilities for your organization KEY FEATURES Get familiar with the fundamentals of data architecture and Cloud computing. Design and deploy enterprise data architectures on the Cloud. Learn how to leverage AI/ML to gain insights from data. DESCRIPTION Clo
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As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call
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Take a data-first and use-case–driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-
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There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilisti
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This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks.Each
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AI is a tool that demands a deeper understanding of where and how to use it. By becoming Citizens of Data Science, we can actively participate in where and how AI is used to improve the human condition. With the help of this guide, you'll get to grips with the relevant AI and data concepts that will help you make the most of the AI revolution.
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With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database model
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Incredibly useful, knowledge graphs help organizations keep track of medical research, cybersecurity threat intelligence, GDPR compliance, web user engagement, and much more. They do so by storing interlinked descriptions of entities—objects, events, situations, or abstract concepts—and encoding the underlying information. How do you cr
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Learn how to deploy complex machine learning models on single board computers, mobile phones, and microcontrollers KEY FEATURES Gain a comprehensive understanding of TinyML's core concepts. Learn how to design your own TinyML applications from the ground up. Explore cutting-edge models, hardware, and software platforms for developing TinyML.
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A guide to understand the basics of machine learning and its application in the field of education KEY FEATURES Create a more efficient and effective learning environment that meets the needs of all students. Learn how to use the profound Machine learning advancements in the field of education. Understand how students can use machine learning
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A comprehensive guide to Deep Learning for Beginners KEY FEATURES Learn how to design your own neural network efficiently. Learn how to build and train Recurrent Neural Networks (RNNs). Understand how encoding and decoding work in Deep Neural Networks. DESCRIPTION Deep Learning has become increasingly important due to the growing need to pro
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Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differenti
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The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners
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A step-by-step guide that will teach you how to deploy TinyML on microcontrollers KEY FEATURES Deploy machine learning models on edge devices with ease. Leverage pre-built AI models and deploy them without writing any code. Create smart and efficient IoT solutions with TinyML. DESCRIPTION TinyML, or Tiny Machine Learning, is used to enable m
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A multifaceted approach to develop an understanding of AI and its potential applications KEY FEATURES AI-informed focuses on AI foundation, applications, and methodologies. AI-inquired focuses on computational thinking and bias awareness. AI-innovate focuses on creative and critical thinking and the Capstone project. DESCRIPTION AI is a dis
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Learn how to use AutoML to leverage Machine Learning for solving business problems KEY FEATURES Get familiar with the common machine learning problems and understand how to solve them. Understand the importance of different types of data and how to work with them effectively. Learn how to use machine learning and AutoML tools to solve real-wor
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A complete guide that will help you get familiar with Machine Learning models, algorithms, and optimization techniques KEY FEATURES Understand the core concepts and algorithms of Machine Learning. Get started with your Machine Learning career with this easy-to-understand guide. Discover different Machine Learning use cases across different dom
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Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing d
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A practical guide that will help you build AI and ML solutions faster with fewer efforts and no programming knowledge KEY FEATURES Start your journey to become an AI expert today. Learn how to build AI solutions to solve complex problems in your organization. Get familiar with different No-code AI tools and platforms. DESCRIPTION No-Code Ar
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This is a practical workbook that analyzes past Kaggle competitions in hopes of helping readers understand the best practices on how to approach future challenges. With the help of this workbook, readers will experience the case studies as if they themselves were part of the competitions and gain hands-on experience on how to approach their first K
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Bring elasticity and innovation to Machine Learning and AI operations KEY FEATURES Coverage includes a wide range of AWS AI and ML services to help you speedily get fully operational with ML. Packed with real-world examples, practical guides, and expert data science methods for improving AI/ML education on AWS. Includes ready-made, purpose-bui
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Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine
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Explore the Essential Concepts, Tools, and Services in GCP KEY FEATURES Build a solid foundation of the Google Cloud Platform. Work with different AI and Machine Learning services offered by Google Cloud. Learn how to use Google cloud services to build scalable apps. DESCRIPTION Google Cloud platform has a suite of cloud computing services f
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Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target--from ultra-low pow
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Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory.Engineers, data s
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With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robus
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A step-by-step guide to get started with Machine Learning KEY FEATURES Understand different types of Machine Learning like Supervised, Unsupervised, Semi-supervised, and Reinforcement learning. Learn how to implement Machine Learning algorithms effectively and efficiently. Get familiar with the various libraries & tools for Machine Learning.
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Unlock the Potential of Data Science and Machine Learning to Your Business and Organization KEY FEATURES Includes today's most popular applications powered by data science and machine learning technology. A solid primer on the entire data science lifecycle, detailed with examples. An integrated approach to demonstrating the use of Image Proces
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This practical guide bridges the gap between general cloud computing architecture in Microsoft Azure and scientific computing for bioinformatics and genomics. You'll get a solid understanding of the architecture patterns and services that are offered in Azure and how they might be used in your bioinformatics practice. You'll get code examples that
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While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment,
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Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does.Explainability methods provide an essential toolkit for better understanding model behavi
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If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professi
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Healthcare is the next frontier for data science. Using the latest in machine learning, deep learning, and natural language processing, you'll be able to solve healthcare's most pressing problems: reducing cost of care, ensuring patients get the best treatment, and increasing accessibility for the underserved. But first, you have to learn how to ac
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Explore the IoT with other emerging technologies like Artificial Intelligence, Machine Learning, Cloud Computing, and Blockchain Technology KEY FEATURES Learn from the basics of Internet of Everything, Industry 4.0, and Society 5.0. Check your knowledge with self-assessment questions and case studies. Supports the Government of India project D
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Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow KEY FEATURES Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical. Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects. Everything is concise, up-to-date, and visually
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Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.RStudio enginee
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Enable data and AI workloads with absolute security and scalability KEY FEATURES Detailed, step-by-step instructions for every data professional starting a career with data engineering. Access to DevOps, Machine Learning, and Analytics wirthin a single unified platform. Includes design considerations and security best practices for efficient u
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Develop AI based real-world Applications KEY FEATURES Provides a practical understanding of AI, including its concepts, tools and techniques. Includes step-by-by-step instructions for implementing machine learning and deep learning algorithms and features. Complex datasets and examples are used to expose mathematical illustrative and pseudo-co
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Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early
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The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems
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Use business intelligence to power corporate growth, increase efficiency, and improve corporate decision making. With this practical book featuring hands-on examples in Power BI with basic Python and R code, you'll explore the most relevant AI use cases for BI, including improved forecasting, automated classification, and AI-powered recommendations
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Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learni
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Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights i
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We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine le
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Explore the cosmic secrets of Distributed Processing for Deep Learning applications. KEY FEATURES In-depth practical demonstration of ML/DL concepts using Distributed Framework. Covers graphical illustrations and visual explanations for ML/DL pipelines. Includes live codebase for each of NLP, computer vision and machine learning applications.
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Explore Azure AI Platform Key Features Easy-to-follow tutorial for getting started with the Azure AI platform. Integrated platform for developing, deploying, and managing AI apps. Includes real-world scenarios and use-cases to fully explore Azure AI Platform. Description Microsoft Azure AI A Beginner's Guide explains the fundamentals of Azure
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Take advantage of the power of cloud and the latest AI techniques. Whether you’re an experienced developer wanting to improve your app with AI-powered features or you want to make a business process smarter by getting AI to do some of the work, this book's got you covered. Authors Anand Raman, Chris Hoder, Simon Bisson, and Mary Branscombe sh
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Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark.In each chapter,
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Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP.Throughout this updated second edition, you'll work thr
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Make use of the most advanced machine learning techniques to perform NLP and feature extraction Key Features Learn about pre-trained models, deep learning, and transfer learning for NLP applications. All-in-one knowledge guide for feature engineering, NLP models, and pre-processing techniques. Includes use cases, enterprise deployments, and a r
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Work with TensorFlow and Keras for real performance of deep learning Key Features Combines theory and implementation with in-detail use-cases. Coverage on both, TensorFlow 1.x and 2.x with elaborated concepts. Exposure to Distributed Training, GANs and Reinforcement Learning. Description Mastering TensorFlow 2.x is a must to read and practice
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A beginners guide to learn ROS, robotics platform, and practice building robotics system Key Features A step-by-step guide covering the robot's design, assembly, navigation and control. Numerous techniques, ROS packages, object detection and image processing concepts included. Practical exercises and sample codes to robotics design, simulation,
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Implementing ML pipelines using MLOps Key Features In-depth knowledge of MLOps, including recommendations for tools and processes. Includes only open-source cloud-agnostic tools for demonstrating MLOps. Covers end-to-end examples of implementing the whole process on Google Cloud Platform. Description This book will provide you with an in-depth
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Build machine learning models and train them to make Android applications much smarter. Key Features Learn by doing, training, and evaluating your own machine learning models. Includes pre-trained TensorFlow models for image processing. Explains practical use cases of artificial intelligence in Android. Description This book features technique
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A Complete Data Analytics Guide for Learners and Professionals. Key Features Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database. Dive into Machine Learning, its tools, and applications. Coverage of applications of Big Data, Data Analysis, and Business Intelligence. Description These days critical problem solving related to dat
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Covers Data Science concepts, processes, and the real-world hands-on use cases. Key Features Covers the journey from a basic programmer to an effective Data Science developer. Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. Implementation of MLOps using Microsoft Azure DevOps. Description "How is the Data Science
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Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-base
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Master the ML process, from pipeline development to model deployment in production. Key Features Prime focus on feature-engineering, model-exploration & optimization, dataops, ML pipeline, and scaling ML API. A step-by-step approach to cover every data science task with utmost efficiency and highest performance. Access to advanced data engineer
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rives next generation path with latest design techniques and methods in the fields of AI and Deep Learning Key Features Extensive examples of Machine Learning and Deep Learning principles. Includes graphical demonstrations and visual tutorials for various libraries, configurations, and settings. Numerous use cases with the code snippets and exa
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Build production-ready machine learning and NLP systems using functional programming, development platforms, and cloud deployment. Key Features In-depth explanation and code samples highlighting the features of the Julia language. Extensive coverage of the Julia development ecosystem, package management, DevOps environment integration, and perfo
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Build AI applications using Python to intelligently interact with the world around you. Key Features Covers the practical aspects of Machine Learning and Deep Learning concepts with the help of this example-rich guide to Python. Includes graphical illustrations of Natural Language Processing and its implementation in NLTK. Covers deep learning
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Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitor
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Guide to Marketing Automation and Accelerated ROI on Advertising Key Features Demonstrates how a DSP works, its bidding strategies, impression tracking, and configurations. Exemplifies how AI/ML simplifies bidding strategies. Illustrates how SSP, exchange, ad-server, and header-bidding (client and server-side) work in detail. Description This
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Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create product
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Concepts of Machine Learning with Practical Approaches. Key Features Includes real-scenario examples to explain the working of Machine Learning algorithms. Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. Full of Python codes, numerous exercises, and model question papers for data scie
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Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine l
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A problem-focused guide for tackling industrial machine learning issues with methods and frameworks chosen by experts. Key Features Popular techniques for problem formulation, data collection, and data cleaning in machine learning. Comprehensive and useful machine learning tools such as MLFlow, Streamlit, and many more. Covers numerous machine
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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 T
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This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed wit
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AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android.Laurence Moroney, lead AI advocate at Google
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This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introd
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Explore the complex app development concepts for iOS application programming with fun and ease. Key Features In-depth knowledge with practical examples on how to develop professional iOS apps. Includes coverage on the entire iOS application development, right from designing the UI to application deployment. Get to know more about machine learni
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This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.When and why would you feed training data as using NumPy or a streamin
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Discover one of the most complete dictionaries in data science. Key Features Simplified understanding of complex concepts, terms, terminologies, and techniques. Combined glossary of machine learning, mathematics, and statistics. Chronologically arranged A-Z keywords with brief description. Description This pocket guide is a must for all data
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How to minimize the global problem of e-waste Key Features Explore core concepts of Reliability Analysis, various smart models, different electronic components, and practical use of MATLAB. Cutting edge coverage on building intelligent systems for reliability analysis. Includes numerous techniques and methods to identify failure and reliabilit
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Utilize Python and IBM Watson to put real-life use cases into production. Key Features Use of popular Python packages for building Machine Learning solutions from scratch. Practice various IBM Watson Machine Learning tools for Computer Vision and Natural Language Processing applications. Expert-led best practices to put your Machine Learning s
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If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and
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Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learningand the web--provides a hands-on end-to-end approach to Tenso
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Explore Machine Learning Techniques, Different Predictive Models, and its Applications Key Features Extensive coverage of real examples on implementation and working of ML models. Includes different strategies used in Machine Learning by leading data scientists. Focuses on Machine Learning concepts and their evolution to algorithms. Descripti
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State-of-the-art BERT implementation for text classification Description This book provides a solid foundation for Natural Language Processing with pragmatic explanation and implementation of a wide variety of industry wide scenarios. After reading this book, one can simply jump to solve real world problems and join the league of NLP developers.
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With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in
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