An Introduction to Intrusion Detection Using ML And Python
Learn how to build intrusion detection models using Python and machine learning. Master data preparation and classification metrics for effective performance. Ideal for cybersecurity and machine learning enthusiasts.
What you’ll learn
- Introduce you to Intrusion Detection.
- Use python and machine learning for intrusion detection.
- Identify a machine learning problem type if classification or regression.
- Master best practice for machine learning data preparation.
This course will introduce you to the intrusion detection domain and how to use machine learning algorithms to build intrusion detection models with best practices. You will get a solid foundation for using applied machine learning for intrusion detection.
This course will give you the resources to learn intrusion detection using Python and Machine learning
By the end of this course, you will:
· You’ll get a complete understanding of how to build intrusion detection models.
· You will have access to the source code of the course project explained step by step.
· Have an understanding of the power of Python and machine learning for intrusion detection.
· Know how to formulate machine learning problems and identify the correct problem type classification or regression.
· Know how to use pandas and matplotlib to check dataset imbalance status.
· Know how to implement the Gini index feature selection algorithm through the ITMO-FS library.
· Use the Gini index and set the threshold to reduce feature space.
· Have a solid understanding of classification metrics and how to access the model’s performance effectively.
· Master the data preparation steps and know how the data preparation process can positively or negatively impact the model’s overall performance.
· Have a clear idea about the decision tree algorithm and its effectiveness in intrusion detection.
Who this course is for:
- Anyone who is interested in cybersecurity and machine learning.
- Intrusion detection researchers.
