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.
Learning Objectives
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.