Unit 4: AI and Big Data

Introduction to AI and Big Data Unit

Welcome to the AI and Big Data unit, a comprehensive course designed to equip you with essential skills in AI and Big Data. This unit is part of the curriculum within the FabLabs project, aimed at providing practical, hands-on learning experiences.

Levels

In this unit, as troughout the whole course, there will be contents for two levels: Basic and Advanced. It is suggested that you take the introductory knowledge test to check your level, and work on the different modules and sections according to your needs and preferences.

Each module and section will indicate its prerequisites, so you will have an idea of what you need to know before starting it.

Content Modules

Here you have a full list of contents of this unit, structured in modules and sections, each with an indication of its complexity level.

Module 1

Data Tools for AI

1.1 Numpy	1.3 Matplotlib
1.1.1 Introduction to NumPy	1.3.1 Introduction to Matplotlib
1.1.2 Creating and manipulating arrays	1.3.2 Creating basic plots
1.1.3. Basic operations	1.3.3 Customizing plots
1.1.4 Manipulating and transforming arrays	1.3.4 Creating subplots and layouts
1.1.5 Advanced operations	1.3.5 Advanced data visualization techniques

1.2 Pandas
1.2.1 Introduction to Pandas
1.2.2 Data structures in Pandas
1.2.3 Basic operations with Pandas
1.2.4 Data Analysis and Manipulation in Pandas

Module 2

Machine Learning

2.1 Classification and Regression

2.2 Introduction to Machine learning

2.2.1 Classification algoritms

2.2.2 Regression techniques

Module 3

Deep learning and Neural Networks

3.1 Basics of neural networks

3.2 Deep learning frameworks

3.3 Building and training neural networks

Module 4

History of AI and Ethics

4.1 AI History and ethical considerations

4.2. Ethical considerations in AI

4.3 Future of AI and ethical implications

Each module is designed to build on the knowledge gained in the previous ones, taking you from the basics of data handling to the complexities of deep learning and ethical considerations in AI. By the end of this course, you will have a solid foundation in artificial intelligence, equipped with both theoretical knowledge and practical skills.

Methodology

Practical Exercises and Projects

Throughout the course, you will engage in practical exercises and projects that reinforce the theoretical concepts covered. Each module includes Jupyter/Colab notebooks where you can practice coding and apply what you have learned. These exercises are designed to help you develop proficiency in using NumPy, Pandas, and Matplotlib for real-world data analysis and visualization tasks.

Interactive Learning

The course also features interactive video content with embedded exercises, providing a dynamic learning experience. You will have the opportunity to test your knowledge through quizzes and assess your progress with multiple-choice questions at the end of each module.

Workshops and Applications

In addition to the theoretical and practical components, the course includes workshops that demonstrate how to apply your skills in a FabLab environment. You will learn to use data tools to work with hardware commonly found in FabLabs, bridging the gap between software and practical applications.

Evaluation

At the end of the course, you will complete a final assessment to evaluate your understanding of the material. This assessment will help you gauge your proficiency and readiness to apply your new skills in real-world scenarios.

We hope you find this course engaging and informative, and that it inspires you to further explore the exciting world of AI within the FabLab environment and beyond.

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