Outlier Detection In Python
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Outlier Detection In Python
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Author : Brett Kennedy
language : en
Publisher: Simon and Schuster
Release Date : 2025-01-07
Outlier Detection In Python written by Brett Kennedy and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-07 with Computers categories.
Learn how to identify the unusual, interesting, extreme, or inaccurate parts of your data. Data scientists have two main tasks: finding patterns in data and finding the exceptions. These outliers are often the most informative parts of data, revealing hidden insights, novel patterns, and potential problems. Outlier Detection in Python is a practical guide to spotting the parts of a dataset that deviate from the norm, even when they're hidden or intertwined among the expected data points. In Outlier Detection in Python you'll learn how to: • Use standard Python libraries to identify outliers • Select the most appropriate detection methods • Combine multiple outlier detection methods for improved results • Interpret your results effectively • Work with numeric, categorical, time series, and text data Outlier detection is a vital tool for modern business, whether it's discovering new products, expanding markets, or flagging fraud and other suspicious activities. This guide presents the core tools for outlier detection, as well as techniques utilizing the Python data stack familiar to data scientists. To get started, you'll only need a basic understanding of statistics and the Python data ecosystem. About the technology Outliers—values that appear inconsistent with the rest of your data—can be the key to identifying fraud, performing a security audit, spotting bot activity, or just assessing the quality of a dataset. This unique guide introduces the outlier detection tools, techniques, and algorithms you’ll need to find, understand, and respond to the anomalies in your data. About the book Outlier Detection in Python illustrates the principles and practices of outlier detection with diverse real-world examples including social media, finance, network logs, and other important domains. You’ll explore a comprehensive set of statistical methods and machine learning approaches to identify and interpret the unexpected values in tabular, text, time series, and image data. Along the way, you’ll explore scikit-learn and PyOD, apply key OD algorithms, and add some high value techniques for real world OD scenarios to your toolkit. What's inside • Python libraries to identify outliers • Combine outlier detection methods • Interpret your results About the reader For Python programmers familiar with tools like pandas and NumPy, and the basics of statistics. About the author Brett Kennedy is a data scientist with over thirty years’ experience in software development and data science. Table fo Contents Part 1 1 Introducing outlier detection 2 Simple outlier detection 3 Machine learning-based outlier detection 4 The outlier detection process Part 2 5 Outlier detection using scikit-learn 6 The PyOD library 7 Additional libraries and algorithms for outlier detection Part 3 8 Evaluating detectors and parameters 9 Working with specific data types 10 Handling very large and very small datasets 11 Synthetic data for outlier detection 12 Collective outliers 13 Explainable outlier detection 14 Ensembles of outlier detectors 15 Working with outlier detection predictions Part 4 16 Deep learning-based outlier detection 17 Time-series data
Anomaly Detection In Video Surveillance
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Author : Xiaochun Wang
language : en
Publisher: Springer Nature
Release Date : 2024-08-06
Anomaly Detection In Video Surveillance written by Xiaochun Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-06 with Computers categories.
Anomaly detection in video surveillance stands at the core of numerous real-world applications that have broad impact and generate significant academic and industrial value. The key advantage of writing the book at this point in time is that the vast amount of work done by computer scientists over the last few decades has remained largely untouched by a formal book on the subject, although these techniques significantly advance existing methods of image and video analysis and understanding by taking advantage of anomaly detection in the data mining community and visual analysis in the computer vision community. The proposed book provides a comprehensive coverage of the advances in video based anomaly detection, including topics such as the theories of anomaly detection and machine perception for the functional analysis of abnormal events in general, the identification of abnormal behaviour and crowd abnormal behaviour in particular, the current understanding of computer vision development, and the application of this present understanding towards improving video-based anomaly detection in theory and coding with OpenCV. The book also provides a perspective on deep learning on human action recognition and behaviour analysis, laying the groundwork for future advances in these areas. Overall, the chapters of this book have been carefully organized with extensive bibliographic notes attached to each chapter. One of the goals is to provide the first systematic and comprehensive description of the range of data-driven solutions currently being developed up to date for such purposes. Another is to serve a dual purpose so that students and practitioners can use it as a textbook while researchers can use it as a reference book. A final goal is to provide a comprehensive exposition of the topic of anomaly detection in video media from multiple points of view.
Time Series Analysis With Python Cookbook
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Author : Tarek A. Atwan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2026-01-16
Time Series Analysis With Python Cookbook written by Tarek A. Atwan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-16 with Computers categories.
Perform time series analysis and forecasting confidently with this Python code bank and reference manual. Access exclusive GitHub bonus chapters and hands-on recipes covering Python setup, probabilistic deep learning forecasts, frequency-domain analysis, large-scale data handling, databases, InfluxDB, and advanced visualizations. Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms Learn different techniques for evaluating, diagnosing, and optimizing your models Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities Book DescriptionTo use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples. You'll hit the ground running by ingesting time series data from various sources and formats and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods. Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.What you will learn Understand what makes time series data different from other data Apply imputation and interpolation strategies to handle missing data Implement an array of models for univariate and multivariate time series Plot interactive time series visualizations using hvPlot Explore state-space models and the unobserved components model (UCM) Detect anomalies using statistical and machine learning methods Forecast complex time series with multiple seasonal patterns Use conformal prediction for constructing prediction intervals for time series Who this book is for This book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want to learn time series analysis and forecasting techniques step by step through practical Python recipes. To get the most out of this book, you’ll need fundamental Python programming knowledge. Prior experience working with time series data to solve business problems will help you to better utilize and apply the recipes more quickly.
Data Mining For Beginners
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Author : Agasti Khatri
language : en
Publisher: Educohack Press
Release Date : 2025-01-03
Data Mining For Beginners written by Agasti Khatri and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.
Data Mining for Beginners: A Programmer’s Guide delves into the world of data mining, a process of discovering patterns and trends in large volumes of data using various algorithms and techniques. This book offers a comprehensive introduction to data mining, focusing on important concepts and their implementation using Python, a popular programming language. We provide step-by-step guidance through Python code to help readers understand and apply data mining techniques. The book covers essential topics like clustering, anomaly detection, data visualization, and processing, making it easier to grasp these concepts and use them in various fields. By the end of the book, readers will be well-versed in data mining concepts and capable of implementing them with Python. What you will learn: • Introduction to data mining and its various concepts. • Data visualization and processing techniques. • The importance of statistics in data mining. • Different data mining algorithms and their implementation in Python. • Cluster analysis and anomaly detection using Python. • Data Cube Technology. • Future trends and research frontiers in data mining. Who the book is for: This book is ideal for programmers seeking to implement data mining algorithms using Python and for students looking for a solid introduction to data mining.
Beginning Anomaly Detection Using Python Based Deep Learning
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Author : Sridhar Alla
language : en
Publisher: Apress
Release Date : 2019-10-10
Beginning Anomaly Detection Using Python Based Deep Learning written by Sridhar Alla and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-10 with Computers categories.
Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics oftime series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You Will Learn Understand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection Who This Book Is For Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection
Performance Evaluation Methodologies And Tools
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Author : Evangelia Kalyvianaki
language : en
Publisher: Springer Nature
Release Date : 2024-01-02
Performance Evaluation Methodologies And Tools written by Evangelia Kalyvianaki and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-02 with Computers categories.
This volume contains the proceedings of the 16th EAI International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2023, which took place in Heraklion, Crete during September 6-7, 2023. The conference brought together researchers, developers, and practitioners from around the world and from different communities including computer science, networks and telecommunications, operations research, optimization, control theory, and manufacturing. The 27 members of the International Program Committee (PC) helped to provide at least 3 reviews for each of the 30 submitted contributions. Based on the reviews and PC discussions, 11 high-quality papers (9 research papers, 1 tool paper, and 1 work-in-progress paper) were accepted to be presented during the conference. The volume includes contributions organized into four thematic sessions: Games and Optimization; Simulation; Networking and Queues; Tools.
Ai Assurance
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Author : Feras A. Batarseh
language : en
Publisher: Academic Press
Release Date : 2022-10-12
Ai Assurance written by Feras A. Batarseh and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-12 with Science categories.
AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI provides readers with solutions and a foundational understanding of the methods that can be applied to test AI systems and provide assurance. Anyone developing software systems with intelligence, building learning algorithms, or deploying AI to a domain-specific problem (such as allocating cyber breaches, analyzing causation at a smart farm, reducing readmissions at a hospital, ensuring soldiers' safety in the battlefield, or predicting exports of one country to another) will benefit from the methods presented in this book. As AI assurance is now a major piece in AI and engineering research, this book will serve as a guide for researchers, scientists and students in their studies and experimentation. Moreover, as AI is being increasingly discussed and utilized at government and policymaking venues, the assurance of AI systems—as presented in this book—is at the nexus of such debates. - Provides readers with an in-depth understanding of how to develop and apply Artificial Intelligence in a valid, explainable, fair and ethical manner - Includes various AI methods, including Deep Learning, Machine Learning, Reinforcement Learning, Computer Vision, Agent-Based Systems, Natural Language Processing, Text Mining, Predictive Analytics, Prescriptive Analytics, Knowledge-Based Systems, and Evolutionary Algorithms - Presents techniques for efficient and secure development of intelligent systems in a variety of domains, such as healthcare, cybersecurity, government, energy, education, and more - Covers complete example datasets that are associated with the methods and algorithms developed in the book
Uncovering The Unusual
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Author : Barrett Williams
language : en
Publisher: Barrett Williams
Release Date : 2024-11-21
Uncovering The Unusual written by Barrett Williams and has been published by Barrett Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-21 with Computers categories.
**Uncovering the Unusual A Journey into the World of Anomaly Detection** Dive into the captivating realm of data anomalies with "Uncovering the Unusual." This comprehensive eBook unlocks the secrets to understanding, detecting, and interpreting the hidden patterns that defy the norm. Perfect for data enthusiasts and professionals alike, this guide systematically explores the world of anomalies—delivering insights that are crucial in an age dominated by data. Begin your journey by grasping the foundations of anomalies what they are, why they matter, and the common hurdles faced in identifying them. Traverse through the diverse types of anomalies—point, contextual, and collective—and learn how each plays a critical role in data analysis. Immerse yourself in the intricacies of data preprocessing, where crucial steps like data cleaning, feature selection, and normalization set the stage for accurate anomaly detection. Delve into statistical methods and discover how univariate and multivariate analysis, probability distributions, and time series data can spotlight anomalies hiding in plain sight. Embrace the power of machine learning techniques, catering to those keen on employing supervised, unsupervised, and semi-supervised methods to uncover anomalies. Explore the fascinating world of clustering and neural networks, from k-means to advanced autoencoders, each offering unique perspectives on anomaly detection. As datasets grow larger and more complex, learn strategies for tackling big data challenges and executing real-time detection. This eBook also guides you through the wealth of tools and software available, equipping you with practical knowledge of both open-source and commercial solutions. Apply what you've learned across a spectrum of industries. Whether detecting fraud, enhancing network security, or making sense of healthcare data, the comprehensive case studies provided will illuminate real-world applications. The journey concludes with a look to the future—where artificial intelligence, IoT, and ethical considerations offer endless possibilities. "Uncovering the Unusual" is more than a guide; it’s an invitation to embrace the unexpected and redefine what's possible in the world of data.
Finding Ghosts In Your Data
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Author : Kevin Feasel
language : en
Publisher:
Release Date : 2022
Finding Ghosts In Your Data written by Kevin Feasel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Discover key information buried in the noise of data by learning a variety of anomaly detection techniques and using the Python programming language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and outliers are and uses the Gestalt school of psychology to explain just why it is that humans are naturally great at detecting anomalies. From there, you will move into technical definitions of anomalies, moving beyond "I know it when I see it" to defining things in a way that computers can understand. The core of the book involves building a robust, deployable anomaly detection service in Python. You will start with a simple anomaly detection service, which will expand over the course of the book to include a variety of valuable anomaly detection techniques, covering descriptive statistics, clustering, and time series scenarios. Finally, you will compare your anomaly detection service head-to-head with a publicly available cloud offering and see how they perform. The anomaly detection techniques and examples in this book combine psychology, statistics, mathematics, and Python programming in a way that is easily accessible to software developers. They give you an understanding of what anomalies are and why you are naturally a gifted anomaly detector. Then, they help you to translate your human techniques into algorithms that can be used to program computers to automate the process. You'll develop your own anomaly detection service, extend it using a variety of techniques such as including clustering techniques for multivariate analysis and time series techniques for observing data over time, and compare your service head-on against a commercial service. What You Will Learn Understand the intuition behind anomalies Convert your intuition into technical descriptions of anomalous data Detect anomalies using statistical tools, such as distributions, variance and standard deviation, robust statistics, and interquartile range Apply state-of-the-art anomaly detection techniques in the realms of clustering and time series analysis Work with common Python packages for outlier detection and time series analysis, such as scikit-learn, PyOD, and tslearn Develop a project from the ground up which finds anomalies in data, starting with simple arrays of numeric data and expanding to include multivariate inputs and even time series data.
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory