Finding Ghosts in Your Data: Anomaly Detection Techniques with Examples in Python - Image 1

£42.11

Apress Finding Ghosts in Your Data: Anomaly Detection Techniques with Examples in Python

Name: Finding Ghosts in Your Data: Anomaly Detection Techniques with Examples in Python
Brand: Apress
Price: 42.11 GBP
Availability: InStock

AI & Machine Learning

It has never been this cheap. We have no record of a lower price.

£42 today · cheaper than every other day in the last 3 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 1 days • 1 data points (No recent data available)

Historical

Generating forecast...

Not enough data points to display chart (need at least 2 points)

Price Distribution

Price distribution over 1 days • 1 price levels

Days at Price

Price Analysis

Most common price: £42 (1 days, 100.0%)

Price range: £42 - £42

Price levels: 1 different prices over 1 days

Description

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 detectionservice 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 Who This Book Is For For software developers with at least some familiarity with the Python programming language, and who would like to understand the science and some of the statistics behind anomaly detection techniques. Readers are not required to have any formal knowledge of statistics as the book introduces relevant concepts along the way.

Product Specifications

Brand: Apress
Format: paperback
ASIN: 1484288696
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 10 November 2022
Listed Since: 24 August 2022

Barcode

No barcode data available

Apress Finding Ghosts in Your Data: Anomaly Detection Techniques with Examples in Python

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Handbook of Anomaly Detection: Cutting-edge Methods and Hands-On Code Examples

Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch

Outlier Detection in Python

Reverse Clustering: Formulation, Interpretation and Case Studies: 957 (Studies in Computational Intelligence, 957)

Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization

Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video (Springer Theses)

Outlier Analysis

SOME RECENT ADVANCES IN MATHEMATICS AND STATISTICS - PROCEEDINGS OF STATISTICS 2011 CANADA/IMST 2011-FIM XX

Nonparametric Statistics for Applied Research

Big Data Analytics: A Management Perspective: 21 (Studies in Big Data, 21)

Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-driven Approach

Data Science in Theory and Practice: Techniques for Big Data Analytics and Complex Data Sets

Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science

Practical Issues of Intelligent Innovations: 140 (Studies in Systems, Decision and Control, 140)

Modern Data Visualization with R (Chapman & Hall/CRC The R Series)

Tracking and Preventing Diseases with Artificial Intelligence: 206 (Intelligent Systems Reference Library, 206)

Learning Statistics: A Manual for Sociology Students

Data Science and Analytics with Python (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Lecture Notes in Computational Intelligence and Decision Making: 2021 International Scientific Conference "Intellectual Systems of Decision-making and ... and Communications Technologies, 77)

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Springer - Data Analysis and Classification Book