Technical professionals dedicated to advanced network operations and cybersecurity, including Data Scientists, ML Engineers, Security Architects, Network Automation Engineers, and SREs.

Participants should have a solid foundation in Python and basic machine learning concepts (supervised/unsupervised learning, evaluation metrics), along with familiarity in network data. Prior exposure to Scikit-Learn and deep learning frameworks like Keras/TensorFlow or PyTorch is strongly recommended, and completion of Kickstart AI: ML in a Day and Kickstart PyTorch in a Day is preferred.

Course Outline:

1. Foundational Intelligence & The ML Pipeline
2. ML vs DL in Practice: Contextualizing tool choice Sci-kit Learn for baselines and PyTorch for high-dimensional, time-series or unstructured data
3. Operationalizing Data Sources: Reviewing and preparing real-world data, e.g. NetFlow or Syslog/Auth Logs, etc.
4. Ingestion Readiness: Standardization of diverse data streams for machine consumption
5. Lab: Feature Engineering and Data Hygiene
6. Classical ML for Tactical Security & Ops
7. High-Performance Classification: Using Gradient Boosting for accurate, low-latency traffic and malware classification
8. Unsupervised Anomaly Detection: implementation of isolation forest and One-class SVM for zero-day threat and network outlier
9. Model Evaluation: Deep dive into False positive rate vs Detection Rate, understanding Precision & Recall
10. Feature Importance & Model Explainability (XAI): Figure out important features
11. Lab: Using SHAP to interpret a Scikit-Learn model’s decision, crucial for audit
12. Deep Learning: Mastering Network Sequences
13. PyTorch for Structured Data: Using Dataset/Data loaders for performance, and building ANN, data loaders that efficiently feed GPUs for massive datasets
14. Time-Series Threat Modeling (LSTMs): Using RNN/LSTM to analyze sequential user behavior (UBA)

Lab: Sequence Analysis with Bi-LSTM: classify sequence of network events (flows/logs)

Deployment Readiness

Model Deployment & Export: Save and loading PyTorch models

Using ONNX format: Converting PyTorch model to ONNX format for cross-platform deployment

Lab: Model Persistence