This course will provide an overview about the various machine learning algorithms used within threat detection as well as AI model fundamentals, including data collection, feature selection, training, and model evaluation.
Cybersecurity teams face an overwhelming volume of data and evolving threats that are increasingly difficult to detect with traditional tools. This course equips you with the knowledge to apply machine learning algorithms for identifying anomalies and improving threat detection. In this course, Machine Learning Algorithms for Threat Detection, you'll learn about AI algorithms used in cybersecurity and threat detection as well as the basics of AI model creation. First, you'll explore the basics of supervised vs. unsupervised learning. Next, you'll discover direct applications of these learning methods when it comes to finding anomalies in data, including decision trees, random forests, K-means clustering and DBSCAN, and deep learning using neural networks. Finally, you'll learn about the basics of creating AI models, including feature selection on training data, overfitting and underfitting during model training, and ways to evaluate the effectiveness of AI models. By the end of this course, you'll be able to identify key machine learning approaches for threat detection and apply foundational AI concepts to build, train, and evaluate effective cybersecurity models.


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