Welcome to the repository for the Artificial Intelligence and Machine Learning Workshops @ FIU! This repository contains all the code and materials used throughout the workshops.
In the first workshop, we cover the basics of Python programming. This includes essential concepts such as:
- If Statements
- For Loops
- While Loops
- Functions
- Object-Oriented Programming (OOP) Basics
- Libraries (e.g., scikit-learn for machine learning)
In the second workshop, we cover the essential concepts and models for Machine Learning. This includes topics such as:
- AI vs ML vs DL
- Data: Training Data vs Testing Data
- Supervised Learning
- Unsupervised Learning
- K-means Model
- Reinforcement Learning
- K-Nearest-Neighbors Model
In the third workshop, we cover a essential model in Machine Learning called Linear Regression. Then, we implement this model in a House Price Predictor. After that, we introduce concepts for Neural Networks and implement this model in the House Price Predictor. This includes topics such as:
- Linear Regression
- House Price Predictor using Linear Regression
- Intro to Neural Networks
- Implementing Neural Networks in the House Price Predictor
In the last workshop, we build a full project using three AI Models to predict stock prices in the market! Key concepts included are:
- Data Preparation (Pandas, NumPy)
- SVR Model (Scikit-Learn)
- Random Forest Model (Scikit-Learn)
- LSTM Model (Tensorflow)
- Results Evaluation (MatPlotLib)