Models, software, and production systems
Machine Learning Engineer
Combine software foundations with the modeling workflow needed to build reliable machine-learning and AI applications.
By the end
Implement model-backed services, reason about their performance, and make evaluation and serving decisions explicit.
Suggested next course
Start with Python for Data Work Python for Data Work
Turn Python records into trustworthy, analysis-ready tables with NumPy and pandas.
Jump to a phase 3 phases
Build the software foundation
7 courses
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Build the software foundation
7 courses
Learn runtime analysis and the structures most often used in data and serving code.
Learn the concepts
Understand data and modeling
6 courses · 5 practice activities
AI-assisted Coding includedView phaseHide phase
Understand data and modeling
6 courses · 5 practice activities
AI-assisted Coding includedRetrieve training data, reason about uncertainty, train models, and engineer features safely.
Learn the concepts
- SQL & DataFiltering Rows
- SQL & DataGrouping and Aggregation
- SQL & DataJoining Tables
- AI & Machine LearningProbability and Statistical Reasoning
- AI & Machine LearningClassical Machine Learning
- AI & Machine LearningFeature Engineering
Put it into practice
Design model-backed systems
8 courses · 8 practice activities
AI-assisted Coding includedView phaseHide phase
Design model-backed systems
8 courses · 8 practice activities
AI-assisted Coding includedStudy graph dependencies and advanced ML systems before building a serving-oriented project.
Learn the concepts
- AlgorithmsTrees and Binary Search Trees
- AlgorithmsHeaps
- AlgorithmsGraphs
- AlgorithmsTopological Sort
- AlgorithmsDynamic Programming
- AI & Machine LearningRAG and Retrieval Systems
- AI & Machine Learning · OptionalCollaborative Filtering Recommender Systems
- AI & Machine LearningMachine Learning System Design