One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
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Habilidades que você terá
- 5 stars66,43%
- 4 stars22,36%
- 3 stars6,90%
- 2 stars2,51%
- 1 star1,77%
Principais avaliações do APRENDIZAGEM AUTOMÁTICA NA PRÁTICA
Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.
A well descriptive experience for this subject; really steps into how to handle information and how to extract info from them. You need to be prepared with Regression Models, it's the base of it.
Awesome course. Would recommend it, but only to those who have a bit of stats and R background. This definitely helped me get a solid enough understanding of using R for machine learning.
excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!
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