This course provides a complete roadmap to becoming an AI Engineer. Learn to design, build, and deploy intelligent systems by mastering machine learning, deep learning, and MLOps. From foundations to advanced models and real-world deployment, gain the practical skills and strategic insights needed to launch and advance your career in this high-demand field.
What you'll learn
Master core AI, machine learning, and deep learning fundamentals.
Build and train models using TensorFlow, PyTorch, and Scikit-learn.
Create models for Computer Vision, NLP, and Generative AI.
Engineer production systems using MLOps and cloud platforms.
Design robust data processing and feature engineering pipelines.
Deploy scalable models as APIs and monitor their performance.
Optimise models for efficiency with advanced techniques.
Prepare for AI engineering roles and technical interviews.
University courses you can audit for free, with lectures, readings and practice quizzes.
A verified certificate from the university if you pay for it.
From Microsoft, a well-regarded name.
Self-paced: start any time.
Disadvantages
Graded assignments and the certificate need the paid track.
Audit access can expire a few weeks after the course ends.
Learning is free, but the certificate costs money.
Some parts (graded work, certificate) are paid.
Some points apply to every course of this kind; see how we rank.
Free to audit
Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
Paid: Graded assignments and the verified certificate (Certificate $55). Audit access may end after the course closes.
Before you start
Strong programming skills in Python are essential.
Basic knowledge of Linear Algebra and Calculus.
Understanding of core Statistics and Probability concepts.
Foundational familiarity with Machine Learning principles.
Comfort with software tools like Git and terminals.
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