Machine Learning: Theory and Hands-on Practice with Python provides a comprehensive foundation in modern machine learning, spanning predictive modeling, unsupervised learning and visualization, and neural network–based approaches. From building and evaluating interpretable regression and classification models, to uncovering structure in unlabeled data, and ultimately training and applying deep learning architectures, you'll develop industry-relevant skills to understand, apply, and critically assess machine learning techniques used in real-world software engineering and AI systems.
This specialization can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS), Master of Science in Artificial Intelligence (MS-AI), and Master of Science in Data Science (MS-DS) degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Artificial Intelligence: https://hua.dididi.sbs/degrees/ms-artificial-intelligence-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
MS in Data Science: https://hua.dididi.sbs/degrees/master-of-science-data-science-boulder
Praktisches Lernprojekt
Across these courses, you’ll build hands-on expertise in the data analysis and computational foundations of modern AI through mathematically grounded instruction, applied coding assignments, and comprehensive assessments preparing you for real-world machine learning applications.
