Coursera
4 weeks to complete at 10 hours a week
Build ML Systems That Perform in Production. Master model training, optimization, and deployment using PyTorch, TensorFlow, and scikit-learn
Intermediate Python and familiarity with basic ML concepts and neural networks; experience with PyTorch or TensorFlow is helpful
This comprehensive program takes you through the complete machine learning engineering lifecycle, from training your first models to shipping optimized, production-ready systems. You'll develop the technical depth and practical judgment needed to build ML systems that perform reliably at scale. Starting with foundational model training and evaluation, you'll progress through hands-on courses covering hyperparameter tuning, custom neural network design, computer vision, and deep learning optimization. Each course emphasizes real-world workflows using industry-standard tools including PyTorch, TensorFlow, scikit-learn, and SHAP, ensuring the skills you build translate directly to professional ML engineering roles.
Coursera is a leading online learning platform that partners with top universities and companies to offer high-quality, job-relevant courses and programs. This Specialization is designed and taught by experienced machine learning experts, ensuring you develop the practical skills and knowledge needed to succeed as an ML engineer.
By program completion, you'll possess the end-to-end skills to confidently take a machine learning problem from business requirement to deployed, optimized solution, making you a more effective and versatile ML practitioner. You'll be able to apply your skills to a wide range of ML engineering roles, such as Machine Learning Engineer, Data Scientist, AI/ML Specialist, and more.
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