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In this liveProject, you’ll explore the basics of anomaly detection by analyzing a medical dataset using unsupervised learning. You’ll create a model that can determine whether patients referred to a clinic have abnormal thyroid function. To accomplish this, you’ll download and prepare your dataset, and then utilize scikit-learn to compare different anomaly detection algorithms to find the most effective. You are going to use Isolation Forest, the Local Outlier Factor (LOF), One-Class SVM and Robust Covariance.
This liveProject is for Python programmers who are interested in exploring machine learning. To begin this liveProject, you will need to be familiar with the following:
In this liveProject, you’ll master the domain of anomaly detection through exploring various methods.
geekle is based on a wordle clone.