IBACTP® — International Board of AI, Cybersecurity & Technology Professionals
Artificial Intelligence Resources

Machine Learning Guides

From Fundamentals to Production AI

Build practical understanding of the concepts, techniques, and professional practices used throughout the machine learning lifecycle.

Responsible AI research and governance laboratory
Generative AI prompt engineering workspace
AI engineering and MLOps pipeline
Artificial Intelligence Machine Learning Guides
Artificial Intelligence resource centers

Select a center to explore guidance, standards and practitioner resources

Resource Library

From Fundamentals to Production AI

Cybersecurity Soc Analysts
01

From Fundamentals to Production AI

Build practical understanding of the concepts, techniques, and professional practices used throughout the machine learning lifecycle.

The IBACTP® Machine Learning Guides translate technical concepts into accessible professional guidance for practitioners, certification candidates, technology leaders, and organizations.

It Governance Grc Board Review
02

Machine Learning Foundations

Develop an understanding of:

  • Artificial Intelligence vs. Machine Learning
  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning
  • Classification
  • Regression
  • Clustering
  • Feature engineering
  • Training and validation
  • Model selection
Cloud Infrastructure Data Center
03

Data Preparation

Learn how data quality influences AI performance.

Resources address:

  • Data collection
  • Data cleaning
  • Missing values
  • Outlier management
  • Feature selection
  • Feature transformation
  • Data normalization
  • Data labeling
  • Training datasets
  • Data leakage
  • Dataset bias
Handson Tech Lab Cohort
04

Model Development

Professional guides cover:

  • Linear regression
  • Logistic regression
  • Decision trees
  • Random forests
  • Support Vector Machines
  • K-nearest neighbors
  • Ensemble learning
  • Gradient boosting
  • Neural networks
  • Deep learning
Government Defense Cyber Briefing
05

Model Evaluation

Understand how to determine whether a machine learning model is actually performing effectively.

Topics include:

  • Accuracy
  • Precision
  • Recall
  • F1 score
  • Confusion matrices
  • ROC curves
  • AUC
  • Mean Absolute Error
  • Mean Squared Error
  • Cross-validation
  • Overfitting
  • Underfitting
Technology governance and risk review
06

Deep Learning

Explore:

  • Artificial neural networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Transformers
  • Attention mechanisms
  • Transfer learning
  • Natural Language Processing
  • Computer vision
Cybersecurity Threat Intelligence Hub
07

MLOps

Learn how organizations manage AI models after development.

Resources include:

  • Model deployment
  • Model versioning
  • Continuous integration
  • Continuous delivery
  • Model monitoring
  • Data drift
  • Concept drift
  • Performance degradation
  • Model retraining
  • ML pipelines
  • Production AI governance
Remote Online Proctoring Verification
08

Generative AI & LLM Engineering

Specialized guides address:

  • Large Language Models
  • Prompt engineering
  • Embeddings
  • Vector databases
  • Semantic search
  • Retrieval-Augmented Generation
  • Fine-tuning
  • LLM evaluation
  • AI agents
  • Agentic workflows
  • LLMOps

EXPLORE MACHINE LEARNING GUIDES

Artificial Intelligence Resources

Turn Guidance Into Verified Competence

Pair these resources with an IBACTP® credential that validates the competence they describe.