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4.6
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5 Bewertungen
Über den Kurs
This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context.
To be successful, 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 final course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute (Amii).
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1 - 2 von 2 Bewertungen für Optimizing Machine Learning Performance
von Emilija G
•Jan 09, 2020
The whole specialization is extremely useful for people starting in ML. Highly recommended!
von Abdullah A
•Jan 02, 2020
the course is too long and a lot of tasks have been discussed in this course. I believe this not sufficient to discuss a lot of tasks in one course