
Course Level: Foundational
Duration: 5 Sessions (1 Hour Each)
Delivery: Virtual – Live via Microsoft Teams
CPEs: Up to 5 (based on attendance)
Start Date: 09/26/26 (Saturdays)
Time: 10:00 am to 11:00 am ET
Course Overview
ISSA-NOVA is offering a hands-on introductory program designed to provide foundational knowledge in Python-based Machine Learning (ML) for security professionals and other technical practitioners with little to no programming experience.
Artificial Intelligence (AI) enables systems to perform tasks that typically require human intelligence, such as learning from data and making decisions. Machine Learning (ML), a core subset of AI, focuses on developing algorithms that allow systems to learn patterns from data and improve performance over time.
This course introduces participants to essential ML concepts and demonstrates how Python can be used to train, evaluate, and visualize machine learning models. It is a continuation of the Spring 2016 course which focused on the K-NeighborsClassifier (KNN) algorithm and it will introduce the Support Vector Machine algorithm. The ML training program consists of a series of courses which emphasize practical application, enabling participants to work directly with data, build simple models, and understand how ML can be applied in cybersecurity and business contexts.
Through guided instruction, demonstrations, and hands-on exercises, participants will gain experience in training algorithms, interpreting results, and identifying common issues such as false positives and false negatives.
Developed by Constantinos Doskas, VP of Data Science, and Brandy Derogene, Director of Data Science. The course is taught by Brandy Derogene, an experienced technical instructor known for making complex topics accessible and actionable, this course blends instruction, examples, hands-on exercises, and guided troubleshooting to ensure participants gain confidence using Python in real environments.
Course Format
A focused, practical learning experience featuring:
- Instructor-led demonstrations of Python and ML concepts
- Hands-on exercises for building and evaluating models
- Real-world examples relevant to cybersecurity and data analysis
- Interactive Q&A and guided troubleshooting
- Practical applications of ML concepts using Python
Course Materials Include
- Expert live instruction
- Step-by-step demonstrations
- Hands-on lab activities
- Sample datasets and scripts
- Supplemental reference materials
- Session summaries and example code
Learning Objectives
By the end of the program, participants will be able to:
- Understand foundational concepts of Artificial Intelligence and Machine Learning
- Use Python to plot and visualize data
- Describe the history and core principles of Machine Learning
- Explain the types of Support Vector Machines and how kernels are used
- Train the SVM algorithm using Python
- Generate predictions from trained SVM algorithms
- Evaluate model performance using accuracy metrics
- Identify false positives and false negatives in model outputs
- Interpret results and recognize common issues in of the SVM models
High-Level Course Outline
| Week | Date | Topic | Area Covered |
| 1 | Saturday 09/26/26 | Python Refresher | Review of Python basics and environment setup |
| 2 | Saturday 10/03/26 | Data Visualization | Review of basics of plotting and visualizing data |
| 3 | Saturday 10/10/26 | ML Fundamentals | Training SVM linear algorithm and calculating prediction accuracy |
| 4 | Saturday 10/17/26 | ML Fundamentals | Training Non-Linear SVM and analyzing results |
| 5 | Saturday 10/24/26 | Model Analysis & Q&A | Identifying strengths and weaknesses of SVM data processing, and final Q&A |
Who Should Attend
This program is ideal for:
- Cybersecurity professionals exploring AI/ML applications
- Analysts working with data and pattern recognition
- Technical staff seeking foundational ML knowledge
- Individuals interested in applying machine learning in real-world scenarios
- Beginners looking to understand ML without deep programming experience
Pricing
ISSA-NOVA Members: Free
Members of Other ISSA Chapters: $25 per session
Non-Members: $50 per session
Participants may earn up to 5 CPEs and will receive a certificate of completion based on attendance.
Registration
REGISTER: ISSA-NOVA Members
https://docs.google.com/forms/d/e/1FAIpQLSd4qX6p4LUr-qajQN6PlITsaaB23V7fryQDkUzwjT2AECTJHw/viewform?usp=publish-editor
REGISTER: Members of Other ISSA Chapters – $25
https://square.link/u/wxB1BAUb
REGISTER: Non-Members – $50
https://square.link/u/Nbq1APO6
Questions
Questions about the course may be directed to:
datascience@issa-nova.org

