Artificial Intelligence and Machine Learning
Improve efficiency, decision-making, and customer experiences through intelligent systems — hands-on with Python, TensorFlow, PyTorch, and cloud AI services.
Tuition
Next cohort: TBD
Seats: 0/20
Build Models That Solve Real Business Problems
Learn core AI and machine learning concepts, apply techniques with industry-standard tools, and develop models using Python, TensorFlow, Keras, PyTorch, and cloud AI platforms — with a portfolio capstone you can show employers.
Who it's for
Professionals and career-changers who want to improve efficiency, decision-making, and customer experiences through intelligent systems.
Format
12 weeks (72 contact hours) · 2 × 2-hour live sessions each week · cohort size 12–20 · ≈36–60h guided assignments & capstone work
Learning outcomes
- Set up a professional AI workspace with Python, Jupyter, and GitHub
- Explore and clean real datasets with pandas and NumPy
- Train and evaluate regression and classification models
- Apply advanced ML: decision trees, random forests, and clustering
- Build deep learning models with TensorFlow and Keras
- Engineer prompts and build with ChatGPT, Claude, and Gemini
- Build a knowledge assistant with RAG (LangChain + ChromaDB)
- Design AI agents with multi-step, agentic workflows
- Build and deploy an AI web app with the OpenAI API and Streamlit
- Assess AI risk, bias, and governance responsibly
12-Week Plan
4 h live each week with hands-on labs and real deliverables
Session 1 (2h)
The AI ecosystem, the AI project lifecycle, Python environment setup, Jupyter notebooks, and GitHub
Session 2 (2h)
Lab: build your AI development environment (Python, VS Code, Jupyter, GitHub) and your first notebook
Deliverable
AI Workspace Portfolio
Session 1 (2h)
pandas and NumPy, data exploration, and data cleaning
Session 2 (2h)
Lab: analyze a real dataset (sales, HR, or healthcare) — clean the data and generate insights
Deliverable
Data Analysis Report (Portfolio Item #1)
Session 1 (2h)
Regression, classification, features, and labels
Session 2 (2h)
Lab: build a predictive model (employee attrition or house price) with scikit-learn
Deliverable
ML Notebook (Portfolio Item #2)
Session 1 (2h)
Decision trees, random forests, and clustering
Session 2 (2h)
Lab: customer segmentation — build a clustering model and generate business recommendations
Deliverable
Customer Intelligence Report
Session 1 (2h)
Neural networks with TensorFlow and Keras
Session 2 (2h)
Lab: train and evaluate an image classifier
Deliverable
Deep Learning Model
Session 1 (2h)
Large language models — ChatGPT, Claude, and Gemini
Session 2 (2h)
Lab: prompt-engineering challenge — build an AI recruiter, AI tutor, and AI support agent
Deliverable
Prompt Library (Portfolio Item #3)
Session 1 (2h)
Embeddings, vector databases, and retrieval
Session 2 (2h)
Lab: build a company knowledge assistant from your own policies, manuals, and PDFs using LangChain and ChromaDB
Deliverable
AI Knowledge Assistant
Session 1 (2h)
AI agents, agentic workflows, and multi-step reasoning
Session 2 (2h)
Lab: build an AI executive assistant that reads documents, summarizes meetings, and generates reports
Deliverable
Agent Demo
Session 1 (2h)
The OpenAI API, AI APIs, and application integration
Session 2 (2h)
Lab: build and deploy an AI web app (résumé analyzer, cover-letter generator, or support assistant) with Streamlit
Deliverable
Deployed Application (Portfolio Item #4)
Session 1 (2h)
Responsible AI, hallucinations, AI risk, and compliance
Session 2 (2h)
Lab: run an AI risk assessment — evaluate an AI system for risk, bias, and security
Deliverable
AI Governance Report
Session 1 (2h)
Choose a track: AI for Business Analysis, Project Management, Healthcare, or Cybersecurity
Session 2 (2h)
Lab: build an industry-specific AI solution end to end
Deliverable
Industry AI Solution
Session 1 (2h)
Adopt AI for a company: analyze the business problem, build the solution, deploy a prototype, and demonstrate ROI
Session 2 (2h)
Final build, deployment, and executive presentation
Deliverable
AI Business Case, AI Prototype, GitHub repository, Executive Presentation, and Deployment Documentation
Tool Stack
Python, ML frameworks, and cloud AI services used from Week 1
Tuition & enrollment
$1,899
Full refund within 7 days of program start. Partial refund available up to 14 days before start date.