Artificial Intelligence & Machine Learning Applications Specialist Certificate

Artificial Intelligence & Machine Learning Applications Specialist Certificate (384 Hours)

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 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

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

The program aligns with the U.S. Bureau of Labor Statistics (BLS) and O*NET classification

15-2051 – Data Scientists / AI Specialists.

Program Structure (384 Hours)

The program is divided into five distinct modules:

Module	Course Title	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 practical 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

Item	Details
Total Hours	384
Duration	Up to 24 Months
Schedule	4 hours/week
Delivery Format	Instructor-led live classes, Applied project workshops, Capstone evaluation

Tuition & Fees

Item	Cost
Module 2 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 module start	100%
After 1–2 months	50%
After 3–4 months	25%
After 4 months	No refund

**Refund requests must be submitted to info@tek2kareer.com. All refunds are processed within 14 business days.

Admission Requirements

Required:

• 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 is required.
• More than 20% absence will result in failure unless approved.
• Excused absences require documentation (medical/family emergencies).
• Students must make up for missed work.

Grading Policy

Component	Weight
Module Assignments	30%
Quizzes & Projects	30%
Midterm ML Project	20%
Final Capstone Project	20%

**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)

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

Student Equipment Requirements

Laptop with:

• 8–16GB RAM
• Python 3.9+
• GPU access recommended (Colab provided)

Disclaimer

This program is a MI LEO authorized program certificate for Artificial Intelligence &

Machine Learning Applications Specialist Certificate. Completion does not guarantee

employment, but prepares students for industry-recognized AI roles.