Prerequisites

1. Required Skills and Tools

To succeed in this course, you should have strong programming experience in any major language. While Python is the primary language used throughout the course, if you’re proficient in other programming languages, you can quickly pick up Python fundamentals as you progress. You’ll also need to be comfortable with essential development tools including the command line, Git, and Docker basics.

For your development setup, install Anaconda for Python management (particularly recommended for Windows users) and set up the UV package manager for handling virtual environments.

2. What You Don’t Need

Importantly, you do not need prior machine learning experience—the course is designed to take you from beginner to practitioner. You also don’t need an advanced mathematics background, as the course covers basic linear algebra concepts in Module 1, providing all the mathematical foundation you’ll need.

3. Adapting to Your Background

The course is particularly well-suited for data engineers looking to transition into ML engineering or data science roles. If you have an infrastructure background with experience in deployment technologies, you can focus more on the ML concepts and may choose to skip deployment sections you’re already familiar with. For career switchers, the course emphasizes building a strong project portfolio that demonstrates your capabilities to potential employers.

4. Course Context and Technical Framework

The course uses Jupyter notebooks throughout and primarily uses AWS for cloud examples, though concepts transfer easily to other platforms.

The course embraces modern development practices, welcoming AI tools like Cursor and ChatGPT while emphasizing the importance of understanding fundamental concepts. You need to grasp the underlying principles to effectively debug AI-generated code, maintain control over your implementations, and create custom solutions when AI tools fall short.

The course focuses exclusively on Python-based deployment and doesn’t cover Spark ML, keeping the scope focused on the most common industry practices. For projects, you’ll work with datasets containing at least 100 rows, ensuring you have sufficient data for meaningful model development.