Machine Learning Engineer Professional Diploma

About this course

Welcome to the Machine Learning Engineer Professional Diploma — a complete, hands-on journey designed to take you from the fundamentals of programming and machine learning to building and deploying real-world AI systems.

This diploma covers the full AI and Machine Learning development lifecycle. You’ll begin by strengthening your foundations in Python programming, data structures, and Object-Oriented Programming (OOP), followed by the mathematics, statistics, and probability concepts needed to understand how machine learning models work and why they make their predictions.

You’ll then dive into core Machine Learning concepts and algorithms, including Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM). You’ll learn how to prepare and analyze data, perform feature engineering, train models, and evaluate their performance using Python and Scikit-learn.

The journey then moves beyond traditional machine learning into modern AI applications. You’ll explore pretrained AI models, including image generation with Stable Diffusion and speech recognition systems, and learn how AI models can be integrated into practical applications.

A major focus of the diploma is taking models beyond notebooks and making them usable in real applications. You’ll learn how to serve and deploy machine learning models using FastAPI, containerize applications with Docker, and deploy AI systems on AWS. You’ll also explore MicroML, learning how machine learning models can run on resource-constrained embedded devices and microcontrollers.

Throughout the diploma, you’ll work on practical projects such as:

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