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Welcome to the File Repository! This section serves as the central hub for all resources, files, and supplementary materials needed to support the curriculum and enhance understanding of each topic.

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Code:

GitHub - mattia-3rne/Machine-Learning-Curriculum

Slides:

Chapter_1_Machine_Learning_Basics

Chapter_2_Supervised Learning

Chapter_3_Unsupervised_Learning

Chapter 1 | Machine Learning Basics

Chapter 1.1 | Algorithms

This chapter introduces the concept of algorithms and their role in solving problems through step-by-step instructions. Key topics include decision-making and iteration, demonstrated through simple examples such as the Collatz conjecture and other basic algorithms. The focus is on building an understanding of how algorithms function, laying the groundwork for more advanced machine learning techniques.

Chapter 1.2 | Data Utilisation

This part explores the nature of data and its role in machine learning. Focus areas include the different types of data and the importance of preprocessing to ensure accuracy and consistency. The chapter also examines the application of data in supervised and unsupervised learning, highlighting how models utilise information to make predictions or detect patterns.

Chapter 2 | Supervised Learning

Chapter 2.1 | Linear Regression

This program uses linear regression to predict class participation based on caffeine intake, illustrating how machine learning can model relationships between variables. By fitting a line to data points, the project provides an intuitive introduction to predictive modeling and trend analysis.