JP
Reviewed Jul 10, 2026
Very informal theory on ML. Addition to my personal toolbox.
Provides a good background with resources that help understand the concepts, the Math and reasoning
Machine Learning: Theory and Hands-on Practice with Python
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://www.coursera.org/degrees/ms-artificial-intelligence-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
Status: Vision Transformer (ViT)
Status: Embeddings
IntermediateSpecialization
Top reviews across Machine Learning: Theory and Hands-on Practice with Python
BS
Reviewed Jul 21, 2026
Excellent course and lecture! This course really helped me in understanding the concepts of unsupervised learning which can be applied in real-word to understand the data patterns.
AE
Reviewed Jul 4, 2026
Provides a good background with resources that help understand the concepts, the Math and reasoning
MM
Reviewed Mar 24, 2026
The concepts are challenging, but the reference materials, availability of transcripts, and more importantly the TAs are a huge help in making the content understandable and clear.
Learner reviews across Machine Learning: Theory and Hands-on Practice with Python
Showing: 7 of 7
Introduction to Machine Learning: Supervised Learning
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ANTHONY
Course: Introduction to Machine Learning: Supervised Learning
5.0
Reviewed Jul 5, 2026Course: Introduction to Machine Learning: Supervised LearningLucas
Course: Introduction to Machine Learning: Supervised Learning
5.0
Reviewed Jun 28, 2026Course: Introduction to Machine Learning: Supervised LearningExcellent and engaging lectures with clear and application of many core ML regression concepts.
Jayce
Course: Introduction to Machine Learning: Supervised Learning
5.0
Reviewed Jul 10, 2026Course: Introduction to Machine Learning: Supervised LearningVery informal theory on ML. Addition to my personal toolbox.
ziping
Course: Introduction to Machine Learning: Supervised Learning
5.0
Reviewed May 18, 2026Course: Introduction to Machine Learning: Supervised Learninggood materials as a entrance to AI
Michael
Course: Introduction to Machine Learning: Supervised Learning
4.0
Reviewed Mar 25, 2026Course: Introduction to Machine Learning: Supervised LearningThe concepts are challenging, but the reference materials, availability of transcripts, and more importantly the TAs are a huge help in making the content understandable and clear.
Jordan
Course: Introduction to Machine Learning: Supervised Learning
3.0
Reviewed Apr 25, 2026Course: Introduction to Machine Learning: Supervised LearningMany of the quiz questions were worded in a very confusing way. I understood the concepts well, but didn't quite understand what the question was asking. Also, it would have been very helpful if the instructor would have walked through some of the basic programming concepts for each module so that I could better understand how to implement the concepts prior to the programming assignments. But overall, the lectures were well done, and I learned a lot in this course.
La
Course: Introduction to Machine Learning: Supervised Learning
3.0
Reviewed Jun 22, 2026Course: Introduction to Machine Learning: Supervised LearningThis course is OK - I didn't find the videos all that helpful all the time, and the labs can be quite tricky if you do not have programming experience. Reading through the recommended reading was a lifesaver as it explains topics better than the videos do and breaks down formulas and symbols.