Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.


Created by:  University of Michigan

Basic Info
LevelIntermediate
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
4.6 stars
Average User Rating 4.6See what learners said
Syllabus

FAQs
How It Works
Trabajo del curso
Trabajo del curso

Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.

Ayuda de tus compañeros
Ayuda de tus compañeros

Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.

Certificados
Certificados

Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.

Creators
University of Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Pricing
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Accede a los materiales del curso

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Accede a los materiales con calificación

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Recibe una calificación final

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Obtén un Certificado de curso para compartir

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Ratings and Reviews
Rated 4.6 out of 5 of 658 ratings

I thank Dr. Kevyn Collins-Thompson and Coursera team for the excellent course. I have learned valuable skills from the course. Dr. Thompson explained ML concepts very skillfully and made the course fun to follow. Assignments are very well selected and reinforce the class concepts. Over-all the course encourages learner to investigate and apply different ways to do same task. I recommend this course to those who are willing to learn machine learning and can't decide where to start.

I enjoyed this course. Many people comment on the lack of theory, but I think as important as theory is, it is even more important to be able to practically use ML algorithms.

This course will set you up to start doing Kaggle competitions quite adequately. In fact, the final assignment is very similar to a Kaggle competition and open-ended enough to make you really feel like you need to harness what you've learned.

good course for beginners

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses