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Module 2 - ML Fundamentals

Descrición

In this section, we will focus on regression algorithms and model evaluation techniques. Regression is a type of supervised learning used to predict continuous numerical values. We will cover several regression algorithms, including Linear Regression and Polynomial Regression. Additionally, we will discuss model evaluation metrics and techniques to ensure the reliability and accuracy of our models.

What you will learn

In this module, you will explore the fundamental concepts and techniques of classification and regression algorithms in machine learning. You will learn how to build, evaluate, and interpret models for predicting both categorical and continuous outcomes. The module will cover key algorithms such as logistic regression, decision trees, k-nearest neighbors, and linear regression. By the end of this module, you will have a solid understanding of how to apply these algorithms to real-world data, improving your ability to solve complex predictive tasks.

Module 2 structure

This module is structured to provide a comprehensive understanding of Data Tools in AI. It includes several key components to facilitate learning and practical application:

  • Initial Questionnaire: This section aims to gauge the user's existing knowledge of data tools in AI. It helps in tailoring the learning experience according to the user's proficiency level.
  • Introduction ML algorithms: A brief presentation that explains what Data Tools are, their purpose, and the most commonly used tools in the field. This section sets the stage for the more detailed explorations to come.
  • Classification Algorithms: The core of the module is divided into three sections, each focusing on a specific tool: Numpy, Pandas and Matplotlib.
  • Regression Algorithms: After learning about each tool, this section provides exercises to test and reinforce the knowledge acquired throughout the module. Students can apply what they have learned and ensure they understand the material thoroughly.
  • Workshops Design: This part of the module presents designs for several workshops that can be implemented in a FabLab environment. These workshops utilize the tools and hardware commonly found in FabLabs, providing a practical context for the skills learned.
  • Final Knowledge Test: The module concludes with a test similar to the initial questionnaire. This final assessment allows students to answer questions related to the course content and evaluate their learning progress.

We hope this structured approach helps you effectively learn and apply data tools in AI, particularly in the innovative environment of a FabLab. Happy learning!

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