Bộ sách luyện thi ETS 2023 LC&RC
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299,000₫
Sách Finding Ghosts in Your Data Anomaly Detection Techniques with Examples in Python (sách keo gáy, bìa mềm)
Categories:Computers - Algorithms and Data Structures
Year:2022
Edition:1
Language:english
Pages:350
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
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.
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