Module 1: Introduction

1.1 Introduction to Machine Learning 1.2 ML vs Rule-Based Systems 1.3 Supervised Machine Learning 1.4 CRISP-DM 1.5 The Modelling Step (Model Selection Process) 1.6 Setting up the Environment 1.7 Introduction to NumPy 1.8 Linear Algebra Refresher 1.9 Introduction to Pandas 1.10 Summary 1.11 Homework

module content on github: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/master/01-intro

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Table of contents

• What is Machine Learning?
• Machine Learning vs Rule-Based Systems: What is the Difference?
• What is Supervised Machine Learning?
• What is CRISP-DM?
• Model Selection Process
• GitHub Codespaces
• Introduction to NumPy
• Linear Algebra Refresher
• Introduction to Pandas
• Summary