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Learner Reviews & Feedback for Data for Machine Learning by Alberta Machine Intelligence Institute

4.4
stars
94 ratings

About the Course

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....

Top reviews

PN

Dec 29, 2020

Excellent depth in coverage. Lab, although only one, was instructive to enable learning while also being exhaustive and intensive to drive learnings home.

BS

Oct 11, 2020

Some bugs in the assignment, but overall excellent discussion of how to avoid common pitfalls when using data for ML.

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