Artificial Intelligence & Machine Learning Applications Specialist Certificate

KaizeNova Technologies LLC d/b/a TEK2KAREER is a proprietary, non-degree–granting institution licensed (license pending) by the Michigan Department of Labor & Economic Opportunity – Workforce Development, Postsecondary Schools Unit (PSS).
This program is an approved vocational certificate program and prepares students for occupational roles in Artificial Intelligence and Machine Learning applications.

PROGRAM OVERVIEW
The Artificial Intelligence & Machine Learning Applications Specialist Certificate is a
comprehensive, 384-hour program designed to prepare learners for high-demand AI, ML, and data automation careers.
This program provides both theoretical foundations and extensive hands-on practice in:
• Python programming for AI
• Data analysis, statistical modeling, and automation
• Machine learning fundamentals
• Deep learning (CNNs, RNNs, transformers)
• NLP and Generative AI
• MLOps, model deployment, and real-world AI integration
Students complete five structured modules, each aligned with industry job expectations and U.S.
Department of Education CIP guidance under Artificial Intelligence (11.0102).

OCCUPATIONAL OBJECTIVES
Graduates will be prepared for roles including:
• Artificial Intelligence Applications Specialist
• Machine Learning Technician / Associate Engineer
• Data Science Technician
• AI Integration Analyst
• Automation & Model Deployment Associate
• ML Ops Assistant
• AI Product Support Specialist
(O*NET 15-2051 — Data Scientists / AI Specialists)

PROGRAM STRUCTURE
Module Course Title / Focus Hours
Module 1 Python & Data Foundations for AI 64 hrs
Module 2 Machine Learning Fundamentals 96 hrs
Module 3 Deep Learning & Neural Networks 96 hrs
Module 4 NLP & Generative AI Applications 64 hrs
Module 5 AI Capstone – Model Deployment & MLOps 64 hrs
Total — 384 Hours
DETAILED MODULE DESCRIPTIONS
Module 1 — Python & Data Foundations for AI (64 Hours)
Students learn Python essentials, data structures, automation, and data cleaning using industry
libraries.
Topics Covered:
• Python syntax, functions, logic flows
• NumPy, Pandas, Matplotlib
• Data wrangling, ETL, feature extraction
• Exploratory data analysis (EDA)
• Working with JSON, CSV, APIs
• File automation & preprocessing pipelines
Outcomes:
• Ability to write reusable Python scripts
• Build data pipelines
• Handle large datasets for ML prep

Module 2 — Machine Learning Fundamentals (96 Hours)
Applied machine learning training:
Topics Covered:
• Supervised & unsupervised learning algorithms
• Regression, classification, clustering
• Train/test split, K-fold cross-validation
• Scikit-learn workflows
• Feature engineering
• Bias/variance tradeoff
• Model evaluation: precision, recall, f1, ROC-AUC
Outcomes:
• Build ML models end-to-end
• Evaluate, tune, and optimize algorithms
Module 3 — Deep Learning & Neural Networks (96 Hours)
Students gain hands-on experience with advanced deep learning.
Topics Covered:
• Neural network fundamentals
• TensorFlow & PyTorch
• CNNs (vision models)
• RNNs, LSTM, GRU
• Transfer learning
• Hyperparameter tuning
• GPU acceleration
• Deep learning project workflows
Outcomes:
• Build and train neural networks
• Apply deep learning to image and text tasks
Module 4 — NLP & Generative AI Applications (64 Hours)
Students explore modern text-processing AI and generative models.
Topics Covered:
• NLP preprocessing, tokenization
• Word embeddings (Word2Vec, GloVe)
• Transformer models
• ChatGPT-style architectures
• Prompt engineering fundamentals
• Text classification, summarization
• Sentiment analysis
• Conversational AI & chatbot design
Outcomes:
• Apply NLP to business problems
• Implement transformer-based workflows
Module 5 — AI Capstone: Model Deployment & MLOps (64 Hours)
Students deliver a full AI project, from architecture to deployment.
Topics Covered:
• MLOps fundamentals
• Model deployment using Flask/FastAPI
• RESTful AI inference services
• TensorFlow Serving
• Version control for ML models
• GitHub Actions for ML workflows
• Cloud deployment concepts (AWS / GCP basics)
Capstone Deliverables:
• Deployed AI model
• Documentation + presentation
• Code repository with automation
PROGRAM LENGTH & SCHEDULE
• Total Hours: 384 Hours
• Length: Up to 24 Months
• Schedule: 4 hours/week
Format:
o Instructor-led live classes
o Hands-on labs
o Applied project workshops
o Capstone evaluation

TUITION & FEES
Item Cost
Program Tuition $15,000
Registration Deposit (required) $1,000
Payment Plans
Customized monthly installments available.
Payment Methods
Zelle, Venmo, Stripe, PayPal, Square, Credit/Debit.
REFUND POLICY (LEO-Compliant) Timing Refund
7+ days before course start 100%
After 1–2 months 50%
After 3–4 months 25%
After 4 months No refund
Refund requests submitted to info@tek2kareer.com
All refunds processed within 14 business days.
ADMISSION REQUIREMENTS
• High school diploma / GED
• Basic computer literacy
• English proficiency
• Government-issued ID
• Stable internet + capable computer
• Completion of Enrollment Agreement
Recommended (NOT required):
• Basic Python or programming background
• Basic math/statistics familiarity
ATTENDANCE POLICY
• 80% minimum attendance
• More than 20% absence = failure unless approved
• Excused absences: documented medical/family emergencies
• Students must make up missed work
GRADING POLICY
Component Weight
Module Assignments 30%
Quizzes & Labs 20%
Midterm ML Project 20%
Final Capstone Project 30%
Minimum passing grade: 70%
COMPLETION REQUIREMENTS
To graduate with this state-licensed certificate, students must:
✔ Complete all 5 modules
✔ Maintain ≥70% overall grade
✔ Complete and present the Capstone
✔ Attend at least 80% of sessions
✔ Submit all required assignments
✔ Fulfill tuition obligations
TOOLS & RESOURCES PROVIDED BY SCHOOL
Students receive access to:
• Tutor LMS
• Zoom
• Python IDE (PyCharm/VS Code)
• Google Colab / Jupyter Notebooks
• TensorFlow & PyTorch
• Scikit-learn
• HuggingFace Transformers
• GitHub Repositories
• Dataset libraries (Kaggle, OpenML)
Student Equipment Requirements
Laptop with:
• 8–16GB RAM
• Python 3.9+
• GPU access recommended (Colab provided)
INSTRUCTOR QUALIFICATIONS
AI instructors must have:
• 5–10+ years of AI/ML industry experience
• Proficiency in TensorFlow/PyTorch
• Python expertise
• Model deployment/MLOps experience
• Prior teaching or corporate training background
• Degree in CS, Engineering, Data Science, or equivalent industry experience
DISCLAIMER
This program is a state-licensed vocational certificate approved under – Artificial Intelligence. Completion does not guarantee employment, but prepares students for industry-recognized entrylevel AI roles.