Understand the Basics of Machine Learning in Cybersecurity: Attendees will gain foundational knowledge about machine learning concepts and how they apply to cybersecurity. This includes an overview of different types of ML models (supervised, unsupervised, and reinforcement learning) and their specific use cases in detecting cyber threats. Real-World Applications and Case Studies: Participants will be presented with case studies demonstrating successful implementations of ML models in cyber threat detection. These real-world examples will illustrate the effectiveness of ML in identifying and mitigating threats before they can cause harm. Challenges and Best Practices: The session will also address the challenges in developing and deploying ML models for cybersecurity, including data quality, model bias, and maintaining privacy. Best practices for overcoming these challenges will be discussed to provide a roadmap for effective ML model implementation.