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Real Time Anomaly Detection for Cognitive Intelligence - XenonStack

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Classical Analytics – Around ten years ago, the tools for analytics or the available resources were excel, SQL databases, and similar relatively simple ones when compared to the advanced ones that are available nowadays. The analytics also used to target things like reporting, customer classification, sales trend whether they are going up or down, etc.In this article we will discuss about Real Time Anomaly Detection. As time passed by the amount of data has got a revolutionary explosion with various factors like social media data, transaction records, sensor information, etc. in the past five years. With the increase of data, how data is stored has also changed. It used to be SQL databases the most and analytics used to happen for the same during the ideal time. The analytics also used to be serialized. Later, NoSQL databases started to replace the traditional SQL databases since the data size has become huge and the analysis also changed from serial analytics to parallel processing and distributed systems for quick results.


The ABCs of Data Science Algorithms - InformationWeek

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Today, big and small companies around the world are racing to adopt the latest tools in artificial intelligence and machine learning. While data is often positioned as the blanket cure for every business malady, those who work in the field understand all too well that data science algorithms are never a one-size-fits-all solution. As the field rapidly evolves, there are a growing number of advanced algorithms available for businesses to deploy in their day-to-day operations. From tools based on deep neural networks, clustering algorithms to time-series analysis, these solutions can resolve a wide range of business problems. However, out of this mass of options, the biggest challenge for an organization may be as simple as sourcing the right data and asking the right questions.


Feature Engineering and Dimensionality Reduction

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Udemy course Feature Engineering and Dimensionality Reduction Feature Selection vs Dimensionality Reduction While both methods are used for reducing the number of features in a dataset, there is an important difference. Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension NED New What you'll learn The importance of Feature Engineering and Dimensionality Reduction in Data Science. Practical explanation and live coding with Python. Description Artificial Intelligence (AI) is indispensable these days.


Exploratory Data Analysis: Zero To Hero With Just a Few Lines of Code

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In all of the above mentioned methods, Exploratory Data Analysis (EDA) plays a vital role. EDA is an important phenomenon under Data Analysis used for gaining a better perspective of data aspects like main features of data, relationships that various features hold with each other, identifying which variables are of utmost importance for our problem etc. Descriptive and Diagnostic Analytics play a key role in finding the patterns, how they are related, important features of data etc. This job is accomplished by Data analysts or Data scientists. Then a Machine Learning Engineer builds the model (predictive analytics) and suggests effective strategies (prescriptive analytics). ML Engineers can perform a fair amount of the analysis using EDA with mere 2–3 lines of code in python.



Machine Learning in Power BI using PyCaret - KDnuggets

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Anomaly Detection is a machine learning technique used for identifying rare items, events, or observations by checking for rows in the table that differ significantly from the majority of the rows. Typically, the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problem or error. Some common business use cases for anomaly detection are: Fraud detection (credit cards, insurance, etc.) using financial data.


Framework for Data Preparation Techniques in Machine Learning

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There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.


How to Grid Search Data Preparation Techniques

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Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithms, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise. An alternative approach to data preparation is to grid search a suite of common and commonly useful data preparation techniques to the raw data. This is an alternative philosophy for data preparation that treats data transforms as another hyperparameter of the modeling pipeline to be searched and tuned.


Framework for Data Preparation Techniques in Machine Learning

#artificialintelligence

There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. In some cases, the distribution of the data or the requirements of a machine learning model may suggest the data preparation needed, although this is rarely the case given the complexity and high-dimensionality of the data, the ever-increasing parade of new machine learning algorithms and limited, although human, limitations of the practitioner. Instead, data preparation can be treated as another hyperparameter to tune as part of the modeling pipeline. This raises the question of how to know what data preparation methods to consider in the search, which can feel overwhelming to experts and beginners alike. The solution is to think about the vast field of data preparation in a structured way and systematically evaluate data preparation techniques based on their effect on the raw data.


4 Automatic Outlier Detection Algorithms in Python

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The presence of outliers in a classification or regression dataset can result in a poor fit and lower predictive modeling performance. Identifying and removing outliers is challenging with simple statistical methods for most machine learning datasets given the large number of input variables. Instead, automatic outlier detection methods can be used in the modeling pipeline and compared, just like other data preparation transforms that may be applied to the dataset. In this tutorial, you will discover how to use automatic outlier detection and removal to improve machine learning predictive modeling performance. Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book, with 30 step-by-step tutorials and full Python source code.