IntelliCademy AI/ML Specialist

The AI/ML Specialist course is an intensive training program designed to prepare professionals to support and lead artificial intelligence and machine learning initiatives. Aligned with the

• DoD 8140 AI/ML Specialist (623) work role, this immersive course combines expert instruction,
• hands-on labs and real-world scenarios to develop mission-ready practitioners capable of
• designing, building, evaluating and deploying secure and ethical AI/ML solutions.
• Participants gain foundational and advanced knowledge in AI concepts, machine learning
• techniques, Python-based model development, model evaluation and deployment, cybersecurity
• considerations for AI systems, and responsible AI practices. Through applied exercises in
• Python notebooks and mission-relevant use cases, learners develop practical skills that can be
• applied immediately to operational challenges, decision-making, cybersecurity defenses and
• emerging AI capabilities.
• The course opens with an ungraded 30-question pre-assessment, closes each module with a timed
• quiz (two attempts, 80% to pass), and finishes with a post-test and course survey.

By the end of this course, participants will be able to:

• explain foundational AI, machine learning and deep learning concepts, terminology and operational use cases
• develop, evaluate and refine machine learning models using Python-based tools and techniques
• demonstrate an understanding of AI/ML deployment, operationalization, performance monitoring and lifecycle management
• apply cybersecurity, networking and risk management principles to protect AI systems, data and infrastructure
• assess the ethical, legal and regulatory considerations of AI development and deployment

• Day 1: Foundational Concepts
• Module 0: Course Introduction
  • Course overview, objectives and agenda, plus a 30-question pre-assessment used to baseline
  • knowledge before and after the course.
• Module 1.1: Basics of Data
  • What data is, data types and structures, common data file formats, and data storage.
• Module 1.2: Statistical Foundations
  • Population versus sample, distributions, descriptive statistics, statistical formulae, z-scores and the empirical rule.
  • Inferential statistics, the central limit theorem, confidence intervals, hypothesis testing and
  • statistical tests.
• Module 1.3: AI/ML Introduction
  • Key definitions, roles and skills, the AI/ML lifecycle, train/test split and cross-validation.
  • Hands-on: data preparation exercise.
• Day 2: Machine Learning
• Module 2.1: Bias and Variance
  • Causes and effects - underfitting and overfitting - mitigation strategies and the bias-variance trade-off.
• Module 2.2: Supervised Learning
  • Decision trees, k-nearest neighbors, the perceptron and evaluation metrics.
• Module 2.2.1: Regression
  • Linear and polynomial regression, ensemble methods and regression metrics.
• Module 2.2.2: Classification
  • Binary versus multi-class, logistic regression, ensemble methods, classification metrics, the confusion matrix, multi-class metrics and ROC AUC.
  • Hands-on: regression and classification exercises.
• Day 3: Unsupervised Learning
• Module 2.3: Unsupervised Learning
  • Dimensionality reduction; clustering with k-means, DBSCAN and hierarchical methods; clustering metrics.
  • Anomaly detection with isolation forest and local outlier factor, and association learning.
  • Hands-on: unsupervised learning exercise.
• Day 4: Deep Learning, AI and Deployment
• Module 3: Deep Learning and Artificial Intelligence
  • Emerging technologies and real-world applications, and non-machine-learning applications.
  • Neural networks, the universal approximation theorem, the perceptron, network architecture,
  • activation functions and backpropagation.
  • CNNs, RNNs, LSTMs, GANs and reinforcement learning.
  • Natural language processing, BERT and transformers, large language models and their obstacles, and prompt development and engineering.
• Module 4.1: Hyperparameter Tuning
  • What hyperparameters are and the goals of tuning; grid search, random search, Bayesian
  • optimization and advanced techniques.
• Module 4.2: Interpretability and Explainability
  • White box versus black box models, with techniques and tools for each.
• Module 4.3: Deployment
  • Code modifications, validation and error handling, modular code, pipelines, process
  • serialization, unit tests and code reviews.
  • Deployment types and environments, local tools, Docker, hosting models, cloud services and
  • deployment architectures.
  • Model monitoring, model drift and response strategies.
• Day 5: Cybersecurity, Ethics and Risk
• Module 5: Cybersecurity and Networking
  • Collaboration with cybersecurity teams, the CIA triad, authentication and authorization, and securing APIs.
  • Access control and secrets management, secure coding practices, encryption, secure logging and
  • logging for machine learning.
  • Data privacy and compliance, anomaly detection and adversarial AI/ML.
  • Common threats - data poisoning, SQL injection, phishing and malware.
  • Networking for AI/ML, and cloud and model deployment basics.
• Module 6.1: Ethics and Regulations The CDAO, DoD AI ethical principles and other common ethical principles.
  • AI/ML ethics and regulation in the United States and in other countries.
• Module 6.2: Risk Management
  • Course conclusion, post-test and course survey.

• IT professionals, cybersecurity practitioners, data analysts, engineers, developers and
• technical personnel seeking to expand their AI/ML expertise or move into AI-focused roles.

Typical job titles include:

• Data Scientist
• Machine Learning Engineer
• AI Developer
• Data Engineer
• Applied Scientist
• Business Intelligence Developer
• Robotics Scientist
• Research Scientist
• Statistician

• Familiarity with basic data structures, statistics, mathematics, algebra, calculus, and computer algorithms
• Proficiency in Python programming and utilizing common data science packages (NumPy, pandas, matplotlib, etc.)
• Prior exposure to collecting, analyzing, and manipulating data
• Laptop with Anaconda/Jupyter environment installed (instructions will be provided)