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Opiniones y comentarios de aprendices correspondientes a Data for Machine Learning por parte de Alberta Machine Intelligence Institute

4.7
estrellas
22 calificaciones
5 revisiones

Acerca del Curso

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute....

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1 - 5 de 5 revisiones para Data for Machine Learning

por Emil K

Mar 22, 2020

The instructor is great, but please fix the programming assignment! There are so many typos it's embarassing. Also, the autograder EXPECTS typos in some variable names, so you can't even pass it if your answers are correct.

por Emilija G

Jan 09, 2020

The whole specialization is extremely useful for people starting in ML. Highly recommended!

por Miguel A S M

Dec 01, 2019

What is different about this course is its focus of ML applied to the real world.

por Valerii M

Mar 31, 2020

Nice course!

por Abdullah A

Dec 24, 2019

the course is very powerful and I have jump to higher level regarding data wrangling and how to deal with data. the assessment have some error which can be fixed easily