- Understanding AI (Artificial Intelligence) and its productive application in businesses
- Overview of AI Engineering and the lifecycle of intelligent systems
- Differentiating AI, Data Science, and Machine Learning within AI Engineering
- Fundamentals of automated decision making and data-driven processes in AI development
Software Development for AI Applications
- Python programming for AI: data structures, loops, functions, and error handling
- Version control with Git & GitHub for collaborative AI Engineering projects
- Introduction to object-oriented programming - crucial for scalable AI systems
- Utilizing the Unix shell for workflow automation in AI Engineering environments
Data Science Foundations & Data Analysis in AI Projects
- Exploratory Data Analysis (EDA) with Pandas and SQL for AI preparation
- Data cleaning and feature engineering for robust AI Engineering
- Visualization with Matplotlib and Seaborn - central tools in AI analysis
- EDA mini-project as a practical entry into data-driven AI thinking
Statistics & Machine Learning for AI Engineering
- Basics of regression, classification, and model evaluation for AI systems
- Training/testing splitting, overfitting, and underfitting in AI Engineering processes
- Performance metrics like accuracy, precision, recall, and F1 score in AI contexts
- Practical machine learning modeling with Scikit-Learn for AI applications
Deep Learning & Modern AI Approaches
- Introduction to neural networks - an essential part of modern AI Engineering systems
- Brief entry into NLP and time series analysis for AI-based applications
- Basics of prompt engineering for generative AI
- Understanding recommender systems and clustering in the context of AI solutions
From Data to Deployable AI
- End-to-end understanding: From data sources to a finished AI product
- Building simple data pipelines in the AI Engineering context
- Insight into product development and deployment in the AI domain
- Use of cloud tools for implementing production-ready AI systems
Capstone Mini-Project in the AI Field
- Working on a realistic use case in AI and AI Engineering
- Carrying out all steps: data acquisition, processing, analysis, model development
- Final presentation and assessment by stakeholders - best practices in AI project management
- Teamwork and agile methods to simulate real AI Engineering projects