Home

The AI Applied Program

• Unit I: Introduction to coding tools

  ---

  • 1.1 Introduction to Git & GitHub
  • 1.2 Introduction to Python
  • 1.3 Introduction to Pandas
  • 1.4 Practice with Python and GitHub
  • 1.5 Dialogue with a Professional: Programming in a Job

• Unit II: Introduction to Data Science

  ---

  • 2.1 Current Context and Application of Mathematical Models in AI
  • 2.2 Introduction to Visualization and Descriptive Statistics
  • 2.3 Introduction to Exploratory Data Analysis (EDA)
  • 2.4 Data Cleaning and Processing
  • 2.5 Dialogue with a Professional: Data Science in a Job

• Unit III: Introduction to Machine learning

  ---

  • 3.1 Natural Language Processing Techniques
  • 3.2 Classification and Regression Models: Supervised Learning
  • 3.3 Pattern Identification in Data: Unsupervised Learning Techniques
  • 3.4 Review of Learned Concepts and Q&A
  • 3.5 Dialogue with a Professional: Artificial Intelligence in the Job World

• Unit IV: Final project

  ---

  • 4.1 Teamwork and Project Planning
  • 4.2 Exploratory Data Analysis (EDA) and Data Cleaning
  • 4.3 Training and Application of the Mathematical Model
  • 4.4 Preparation for Data Presentation and Visualization
  • 4.5 Final Course Project Presentation (TFC)

---

Recommended links

• Python courses with certification
• Data Science courses with certification
• Python cheatsheet
• Data case studies