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Machine learning is revolutionizing cybersecurity, providing advanced tools for proactive threat detection and mitigation. Machine Learning and AI in Cybersecurity offers a hands-on approach to integrating AI-driven solutions into cybersecurity frameworks. Starting with a foundational understanding of machine learning and basic Python programming, learners will explore how to build, analyze, and optimize machine learning models tailored for cybersecurity applications. Topics include neural networks, clustering techniques, and self-organizing maps, with real-world use cases such as intrusion detection, anti-malware solutions, log analysis, and vulnerability management.
Led by Dr. Chuck Easttom, a leading cybersecurity expert and inventor with 26 patents, this course is ideal for professionals looking to enhance their cybersecurity skills through machine learning techniques. Whether you're a cybersecurity analyst, software developer, or IT professional, this course provides the tools and knowledge to apply AI in securing digital environments.
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Lesson 1: Introduction to Machine Learning
Machine Learning Concepts
Supervised vs. Unsupervised Learning
Overview of Key Algorithms (Neural Networks, Clustering, KNN, Self-Organizing Maps)
Lesson 2: Basic Python for Machine Learning
Variables and Statements
Object-Oriented Programming
File Handling and Exception Handling
Working with Modules
Lesson 3: Implementing Machine Learning in Cybersecurity with TensorFlow
Introduction to TensorFlow for Cybersecurity Applications
Loading and Processing Data
Building Neural Networks for Threat Detection
K-Means Clustering and KNN for Anomaly Detection
Lesson 4: Advanced AI in Cybersecurity
Large Language Models (LLMs) and Their Security Implications
Using AI APIs for Cybersecurity Applications
Future Trends in AI-Driven Security