Data Insights, Anomalies, & Verification: Handling Anomalies

Data Insights, Anomalies, & Verification: Handling Anomalies

• Course Overview
• list sources of data anomaly and compare the differences between data verification and validation
• describe approaches of facilitating decomposition and forecasting, and list the steps and formulas used to achieve the desired outcome
• recall data examination approaches, and use randomization tests, null hypothesis, and Monte Carlo
• identify anomaly detection scenarios and categories of anomaly detection techniques
• recognize prominent anomaly detection techniques
• demonstrate how to facilitate contextual data and collective anomaly detection using scikit-learn
• list prominent anomaly detection tools and their key components
• recognize essential rules of anomaly detection
• implement anomaly detection using scikit-learn, R, and boxplot

In this 9-video course, learners examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy. The course opens with a thorough look at the sources of data anomaly and comparing differences between data verification and validation. You will then learn about approaches to facilitating data decomposition and forecasting, and steps and formulas used to achieve the desired outcome. Next, recall approaches to data examination and use randomization tests, null hypothesis, and Monte Carlo. Learners will examine anomaly detection scenarios and categories of anomaly detection techniques and how to recognize prominent anomaly detection techniques. Then learn how to facilitate contextual data and collective anomaly detection by using scikit-learn. After moving on to tools, you will explore the most prominent anomaly detection tools and their key components, and recognize the essential rules of anomaly detection. The concluding exercise shows how to implement anomaly detection with scikit-learn, R, and boxplot.

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

• Course Overview
• describe the supervised and unsupervised approaches of anomaly detection
• compare the prominent anomaly detection algorithms
• demonstrate how to detect anomalies using R, RCP, and the devtools package
• identify components of general online anomaly detection systems
• describe the approaches of using time series and windowing to detect anomalies
• recognize the real-world use cases of anomaly detection as well as the steps and approaches adopted to handle the entire process
• demonstrate detecting anomalies using boxplot and scatter plot
• demonstrate the mathematical approaches of detecting anomalies
• implement anomaly detection using a K-means machine learning approach
• implement anomaly detection with visualization, cluster, and mathematical approaches

Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy. This 10-video course begins with an overview of machine learning anomaly detection techniques, by focusing on the supervised and unsupervised approaches of anomaly detection. Then learners compare the prominent anomaly detection algorithms, learning how to detect anomalies by using R, RCP, and the devtools package. Take a look at the components of general online anomaly detection systems and then explore the approaches of using time series and windowing to detect online or real-time anomalies. Examine prominent real-world use cases of anomaly detection, along with learning the steps and approaches adopted to handle the entire process. Learn how to use boxplot and scatter plot for anomaly detection. Look at the mathematical approach to anomaly detection and implementing anomaly detection using a K-means machine learning approach. Conclude your coursework with an exercise on implementing anomaly detection with visualization, cluster, and mathematical approaches.