Machine Learning & AI Developer Certification Framework

Machine Learning & AI Developer Certification Framework

An overview of the competencies, tools, curriculum, and pathways for aspiring Machine Learning & AI Developers.

🎯Learning Outcomes & AI Competencies

Learners who complete this certification will:

  • Master Machine Learning Fundamentals: Understand core concepts of supervised and unsupervised learning, including regression, classification, clustering, and dimensionality reduction.
  • Implement Neural Networks and Deep Learning: Develop neural networks using frameworks like TensorFlow and PyTorch, including CNNs and RNNs.
  • Perform Data Science Tasks: Collect, clean, preprocess, label, and explore data to prepare for model development.
  • Deploy and Manage AI Models: Execute full-cycle model deployment using cloud platforms (AWS, Azure ML, Google Cloud).
  • Evaluate and Optimize Models: Implement performance metrics, tune hyperparameters, and validate models rigorously.
  • Understand Ethical AI Development: Identify and mitigate bias, adhere to data privacy norms, and deploy responsible, explainable AI systems.
  • Collaborate in AI Teams: Document work clearly, engage effectively in teams, and communicate technical findings to non-technical stakeholders.

🛠️Tools, Platforms & Software Mastery

Students will gain proficiency in:

  • Programming Languages: Python (libraries: Pandas, NumPy, Matplotlib, Scikit-learn).
  • Deep Learning Frameworks: TensorFlow, PyTorch, Keras.
  • Cloud Platforms: AWS SageMaker, Azure ML Studio, Google Cloud AI Platform.
  • Development Tools: GitHub, Jupyter Notebooks, VS Code.
  • Data Management Tools: SQL databases, data labeling tools (Label Studio).

📚Course Structure & Instructional Sequence

Module 1: Introduction to AI & ML

Basics of AI, history, key ML concepts, ethics, and career pathways.

Module 2: Data Science Foundations

Data collection, cleaning, exploratory analysis, labeling, and preprocessing.

Module 3: Supervised and Unsupervised Learning

Regression, classification, clustering, model evaluation, cross-validation.

Module 4: Neural Networks & Deep Learning

Neural network architectures, TensorFlow/PyTorch tutorials, CNNs and RNNs.

Module 5: Model Deployment & MLOps

Deploying models using AWS/Azure/Google Cloud, CI/CD pipelines, model monitoring.

Module 6: AI Project Capstone

Real-world project involving conception-to-deployment, documentation, presentation, and ethical review.

📝Performance Assessments, Micro-Credentials & Capstone Projects

Hands-On ML Challenges:

Practical tasks in building and optimizing various ML models.

Micro-Credentials:

  • "Data Science Fundamentals"
  • "Deep Learning Engineer"
  • "MLOps Specialist"

Capstone Project Examples:

  • Healthcare predictive analytics (patient readmission prediction).
  • Environmental AI systems (monitoring red tide, water quality analytics).

🔗Crosswalk to Florida CTE Courses & Certifications

Florida DOE Course Codes Alignment: Integrates with existing IT courses (Programming Essentials, Data Science Foundations, Applications of AI).

Industry Certifications Alignment:
  • AWS Certified Machine Learning – Specialty
  • Microsoft Azure AI Fundamentals (AI-900)
  • Google Cloud Professional ML Engineer

🧑‍🎓Target Learner Levels

  • High School: 11th-12th grade CTE learners with prior foundational programming.
  • Adult Technical Centers: Career changers and upskilling adult learners.
  • State Colleges: Integrated into AS degrees in Computer Science, Data Science, or Applied AI.

🧱Stackability & Articulation Pathways

  • High School to College: Eligible for articulation credits towards A.S. degrees in Applied AI, Computer Science, and Data Science.
  • College Stackability: Serves as a College Credit Certificate (CCC), directly stacking into associate degree programs.
  • Higher Education Pathways: Articulates towards B.A.S./B.S. degrees in Data Analytics, Information Technology, or AI.

🤝Industry Partnerships & Alignment

  • Employer Advisory Board: Involving companies specializing in AI solutions, healthcare, aerospace, logistics, and finance sectors.
  • Partnerships with AI Platforms: Collaboration with AWS, Microsoft Azure, Google Cloud for platform-specific training.
  • Community AI Projects: Participation in GoodSAM.ai initiatives like environmental monitoring and SmartTown.ai civic solutions.
  • Internships and Apprenticeships: Structured internship programs with local AI firms and tech companies.
  • Industry Challenges and Hackathons: Company-sponsored AI challenges and competitions, fostering real-world application of skills.

Machine Learning & AI Developer Certification Framework Overview.