Machine Learning and AI in Cybersecurity (Video Course)

Description

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.

Skill Level

• Beginner
• Intermediate

Learn How To:

• Write and implement common machine learning algorithms
• Optimize and analyze machine learning models for cybersecurity applications
• Apply AI to real-world cybersecurity challenges such as intrusion detection and malware analysis

Course Requirements:

• Basic understanding of cybersecurity concepts
• Some familiarity with Python (introductory Python concepts will be covered)

Who Should Take This Course:

• Cybersecurity professionals looking to integrate machine learning into security operations
• Software developers working in AI, cybersecurity, or IT security
• IT personnel interested in AI-driven security solutions
• Machine learning practitioners exploring cybersecurity applications

Sample Content

Table of Contents

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

Updates

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