Introduction to AI & ML II: Regression

The second workshop of the series: Introduction to AI & ML. Regression is one of the fundamental pillars of Machine Learning or Machine Learning. Regression models are used to predict continuous numerical data, such as the price of a stock, a company’s future sales, or the durability of a vehicle. We will learn what regression is, and we will use some of the most important models to see how they adapt to different prediction tasks depending on their characteristics. For this, we will use Jupyter/Colab, where we will program in Python. We will use libraries such as Pandas and NumPy for data processing and Scikit-Learn for predictive models. You will learn to: - Perform a preliminary analysis of the available data - Split your data into training and testing sets - Train your own regression models - Evaluate the quality of the models If you are interested in data prediction and learning about Machine Learning, don’t hesitate to join! Download Workshop Resources