Big data
James D. Miller
IBM Watson provides fast, intelligent insight in ways that the human brain simply can't match. Through eight varied projects, this book will help you explore the computing and analytical capabilities of IBM Watson.The book begins by refreshing your knowledge of IBM Watson's basic data preparation capabilities, such as adding and exploring data to prepare it for being applied to models. The projects covered in this book can be developed for different industries, including banking, healthcare, media, and security. These projects will enable you to develop an AI mindset and guide you in developing smart data-driven projects, including automating supply chains, analyzing sentiment in social media datasets, and developing personalized recommendations.By the end of this book, you'll have learned how to develop solutions for process automation, and you'll be able to make better data-driven decisions to deliver an excellent customer experience.
Jagjeet Singh Makhija, Charles Odunukwe
The DP-600 exam tests your ability to design and implement analytics solutions using Microsoft Fabric, including planning data analytics environments, managing data integration and security, and optimizing performance. Written by two Microsoft specialists with over three decades of combined experience, this book will help you confidently prepare for the DP-600 exam by teaching you the skills that are essential for effectively implementing and designing analytics solutions.You’ll explore data analytics in Microsoft Fabric in detail and understand foundational topics such as data exploration, SQL querying, and data transformation, alongside advanced techniques such as semantic model optimization, performance tuning, and enterprise-scale model design. The book addresses strategic planning, data integration, security, scalability, and the complete project lifecycle, including version control, deployment, and continuous improvement. You’ll also get to grips with practical SQL integration with Microsoft Fabric components, with mock exams to help you reinforce what you’ve learned.By the end of this book, you’ll be able to plan, implement, and optimize analytics solutions using Microsoft Fabric, and you'll be well-equipped with the practical skills needed to tackle real-world data challenges and pass the DP-600 exam.
Andrew Bell, Francisco Arturo Viveros, Sander Rensen,...
Implementing Oracle API Platform Cloud Service moves from theory to practice using the newest Oracle API management platform. This critical new platform for Oracle developers allows you to interface the complex array of services your clients expect in the modern world.First, you'll learn about Oracle’s new platform and get an overview of it, then you'll see a use case showing the functionality and use of this new platform for Oracle customers. Next, you’ll see the power of Apiary and begin designing your own APIs. From there, you’ll build and run microservices and set up the Oracle API gateways. Moving on, you’ll discover how to customize the developer portal and publish your own APIs. You’ll spend time looking at configuration management on the new platform, and implementing the Oauth 2.0 policy, as well as custom policies. The latest finance modules from Oracle will be examined, with some of the third party alternatives in sight as well.This broad-scoped book completes your journey with a clear examination of how to transition APIs from Oracle API Management 12c to the new Oracle API Platform, so that you can step into the future confidently.
Implementing Qlik Sense. Design, Develop, and Validate BI solutions for consultants
Ganapati Hegde, Kaushik Solanki
Qlik Sense is a leading platform for business intelligence (BI) solutions. Qlik Sense helps organizations in making informed decisions based on the data they have.This book will teach you how to effectively use Qlik for optimum customer satisfaction. You will undergo a metamorphosis from a developer to a consultant who is capable of building the most suitable BI solutions for your clients. The book will take you through several business cases – this will give you enough insight to understand the needs of the client clearly and build a BI solution that meets or exceeds their expectations. Starting from the pre-project activities, you will go to the actual execution of the project, the implementation, and even maintenance. This book will give you all the information you need - from the strategy to requirement gathering to implementing BI solutions using Qlik Sense. The book will empower you to take the right decisions in tricky and diffi cult situations while developing analytics and dashboards.
James D. Miller
Splunk is the leading platform that fosters an efficient methodology and delivers ways to search, monitor, and analyze growing amounts of big data. This book will allow you to implement new services and utilize them to quickly and efficiently process machine-generated big data. We introduce you to all the new features, improvements, and offerings of Splunk 7. We cover the new modules of Splunk: Splunk Cloud and the Machine Learning Toolkit to ease data usage. Furthermore, you will learn to use search terms effectively with Boolean and grouping operators. You will learn not only how to modify your search to make your searches fast but also how to use wildcards efficiently. Later you will learn how to use stats to aggregate values, a chart to turn data, and a time chart to show values over time; you'll also work with fields and chart enhancements and learn how to create a data model with faster data model acceleration. Once this is done, you will learn about XML Dashboards, working with apps, building advanced dashboards, configuring and extending Splunk, advanced deployments, and more. Finally, we teach you how to use the Machine Learning Toolkit and best practices and tips to help you implement Splunk services effectively and efficiently. By the end of this book, you will have learned about the Splunk software as a whole and implemented Splunk services in your tasks at projects
Liyanapathirannahelage H Perera
MapReduce is a technology that enables users to process large datasets and Hadoop is an implementation of MapReduce. We are beginning to see more and more data becoming available, and this hides many insights that might hold key to success or failure. However, MapReduce has the ability to analyze this data and write code to process it.Instant MapReduce Patterns – Hadoop Essentials How-to is a concise introduction to Hadoop and programming with MapReduce. It is aimed to get you started and give you an overall feel for programming with Hadoop so that you will have a well-grounded foundation to understand and solve all of your MapReduce problems as needed.Instant MapReduce Patterns – Hadoop Essentials How-to will start with the configuration of Hadoop before moving on to writing simple examples and discussing MapReduce programming patterns.We will start simply by installing Hadoop and writing a word count program. After which, we will deal with the seven styles of MapReduce programs: analytics, set operations, cross correlation, search, graph, Joins, and clustering. For each case, you will learn the pattern and create a representative example program. The book also provides you with additional pointers to further enhance your Hadoop skills.
Inteligentna sieć. Algorytmy przyszłości. Wydanie II
Douglas McIlwraith, Haralambos Marmanis, Dmitry Babenko
Określenie „inteligentna sieć” może przywodzić na myśl futurystyczną wizję maszyn przejmujących kontrolę nad światem i niszczących ludzkość, jednak w rzeczywistości jest związane z rozwojem technologii. Związane jest z powstawaniem oprogramowania, które potrafi się uczyć i reagować na zachowania użytkowników. Oznacza też projektowanie i implementację inteligencji maszynowej. Inteligentna sieć rozwija się tu i teraz — znajomość zagadnień uczenia maszynowego i budowy inteligentnych algorytmów staje się bardzo potrzebna inżynierom oprogramowania! Niniejsza książka jest przeznaczona dla osób, które chcą projektować inteligentne algorytmy, a przy tym mają podstawy z zakresu programowania, matematyki i statystyki. Przedstawiono tu schematy projektowe i praktyczne przykłady rozwiązań. Opisano algorytmy, które przetwarzają strumienie danych pochodzące z internetu, a także systemy rekomendacji i klasyfikowania danych za pomocą algorytmów statystycznych, sieci neuronowych i uczenia głębokiego. Mimo że przyswojenie tych zagadnień wymaga wysiłku, bardzo ułatwi implementację nowoczesnych, inteligentnych aplikacji! W tej książce między innymi: wprowadzenie do problemów algorytmów inteligentnych systemy rekomendacji i filtrowanie kolaboratywne wykorzystanie regresji logistycznej do wykrywania oszustw uczenie głębokie, uczenie na żywo i renesans sieci neuronowych podejmowanie decyzji perspektywy inteligentnej sieci Inteligentny algorytm wyławia perły w strumieniach danych! Dr Douglas McIlwraith jest ekspertem w dziedzinie uczenia maszynowego. Zajmuje się analizą danych w londyńskiej agencji reklamowej. Prowadził badania w dziedzinach systemów rozproszonych, robotyki i zabezpieczeń. Dr Haralambos Marmanis jest pionierem w obszarze technik uczenia maszynowego w rozwiązaniach przemysłowych. Od 25 lat rozwija profesjonalne oprogramowanie. Dmitry Babenko projektuje złożone systemy dla firm z takich branż, jak bankowość, ubezpieczenia, zarządzanie łańcuchem dostaw i analityka biznesowa.
Santanu Pattanayak, Manohar Swamynathan
This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python.The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI.By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.
Indraneel (Neel) Mitra, Ryan Burke
The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs.This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You’ll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you’ll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance.By the end of this IoT book, you’ll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.
Interactive Applications using Matplotlib
This book is intended for Python programmers who want to do more than just see their data. Experience with GUI toolkits is not required, so this book can be an excellent complement to other GUI programming resources.
Elias Dabbas
Plotly's Dash framework is a life-saver for Python developers who want to develop complete data apps and interactive dashboards without JavaScript, but you'll need to have the right guide to make sure you’re getting the most of it. With the help of this book, you'll be able to explore the functionalities of Dash for visualizing data in different ways.Interactive Dashboards and Data Apps with Plotly and Dash will first give you an overview of the Dash ecosystem, its main packages, and the third-party packages crucial for structuring and building different parts of your apps. You'll learn how to create a basic Dash app and add different features to it.Next, you’ll integrate controls such as dropdowns, checkboxes, sliders, date pickers, and more in the app and then link them to charts and other outputs. Depending on the data you are visualizing, you'll also add several types of charts, including scatter plots, line plots, bar charts, histograms, and maps, as well as explore the options available for customizing them.By the end of this book, you'll have developed the skills you need to create and deploy an interactive dashboard, handle complexities and code refactoring, and understand the process of improving your application.
Serg Masís, Aleksander Molak, Denis Rothman
Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.
Serg Masís
Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf.We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
Introduction to Algorithms. A Comprehensive Guide for Beginners: Unlocking Computational Thinking
Cuantum Technologies LLC
Begin your journey into the fascinating world of algorithms with this comprehensive course. Starting with an introduction to the basics, you will learn about pseudocode and flowcharts, the fundamental tools for representing algorithms. As you progress, you'll delve into the efficiency of algorithms, understanding how to evaluate and optimize them for better performance. The course will also cover various basic algorithm types, providing a solid foundation for further exploration.You will explore specific categories of algorithms, including search and sort algorithms, which are crucial for managing and retrieving data efficiently. You will also learn about graph algorithms, which are essential for solving problems related to networks and relationships. Additionally, the course will introduce you to the data structures commonly used in algorithms.Towards the end, the focus shifts to algorithm design techniques and their real-world applications. You will discover various strategies for creating efficient and effective algorithms and see how these techniques are applied in real-world scenarios. By the end of the course, you will have a thorough understanding of algorithmic principles and be equipped with the skills to apply them in your technical career.
MrExcel's Holy Macro! Books, Liam Bastick
This book serves as a comprehensive guide to financial modeling, equipping readers with the skills and knowledge to create accurate, reliable models for analysis and decision-making. Designed for professionals, students, and finance enthusiasts, it bridges theoretical principles with practical Excel-based techniques, ensuring a balanced and thorough understanding of the subject.Key Excel functions such as SUMPRODUCT, INDEX and MATCH, and LOOKUP are covered in depth, alongside essential tools like conditional formatting, data validation, and solver. The book emphasizes best practices in layout design, error checking, and model transparency, helping users build robust and easy-to-follow financial models. Practical methodologies for time-series analysis, control accounts, and financial statement theory are explored, making it a versatile resource.The step-by-step model-building example guides readers through structuring, linking, and finalizing financial statements, including revenue, expenditure, taxation, and cash flow. Ratio analysis and self-review techniques are also discussed to ensure model accuracy and integrity. This detailed yet accessible guide empowers readers to create professional financial models with confidence and clarity.
Jay Gendron
Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards.After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.