Accelerated AI Training Program

12 weeks · 3 days per week. Focus: Image analysis (computer vision) for manufacturing and welding inspection. The program is organized by capability — each module maps to the chapters and labs that build it.

Monday Instructor-led lecture with hands-on and scenario-based labs
Tuesday Wrap up lectures and labs, then progress into project work
Wednesday Facilitated capstone project work
  1. Week 1

    Module 1 Agile Development Methodology

    In progress

    Learn the mindset, terminology, and process of Agile and apply it to data science work.

    Agile Development Methodology
  2. Week 2

    Module 2 Use Case & Business Understanding

    In progress

    Identify problems, define goals and value, honestly assess whether a problem is a data science problem, and communicate with stakeholders.

    Framing AI Use Cases
  3. Weeks 3–4

    Module 3 Programming

    Available

    Programming basics, development environments, git version control, unit testing, and code reviews.

    Python Basics if-else and Flow Control Loops Functions Debugging Lists Dictionaries and Structuring Data Strings and Text Editing Regular Expressions Command Line Programs Object-Oriented Programming Git and Version Control Testing and Unit Tests
  4. Week 5

    Module 4 Data Handling

    Available

    Read from different data sources and clean, organize, and store data for model training.

    Reading and Writing Files Organizing Files Web Scraping Excel Spreadsheets Google Sheets SQLite Databases PDF and Word Documents CSV, JSON, and XML Files Time, Scheduling, and Launching Programs Sending Emails, Texts, and Push Notifications Graphs and Images Text in Images (OCR) SQL for Data Science Data Engineering and Pipelines
  5. Week 6

    Module 5 Data Exploration

    Available

    Describe datasets with statistics, visualize in aggregate, find patterns, and generate early insights.

    Dataset Statistics and Visualization Communicating with Visualization Teen Mental Health Netflix Titles Catalog
  6. Week 7

    Module 6 Feature Engineering

    Available

    Create variables from existing data, prepare datasets for training and inference, and select features.

    Feature Engineering
  7. Weeks 8–9

    Module 7 AI/ML Training

    Available

    Select the right algorithm, tune models for stronger performance, and use infrastructure like GPUs.

    Algorithm Families Introduction to Neural Networks AI/ML Model Training CNNs and Computer Vision Transformers, RAG Models, and LLMs Dimensionality Reduction Heart Disease Neural Networks CV Lab 01: Image Transforms and Augmentation CV Lab 02: Convolutions and Feature Maps Fruit and Vegetable Classification
  8. Week 10

    Module 8 AI/ML Model Evaluation

    Available

    Measure model performance, investigate where models underperform, and improve interpretability and explainability.

    Model Evaluation CV Lab 03: Model Comparison and Metrics CV Lab 04: Grad-CAM Interpretability
  9. Weeks 11–12

    Module 9 AI/ML Model Deployment & Monitoring

    In progress

    Move models into production, monitor performance on real-world data, and retrain as needed.

    Deployment and Monitoring