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.