Optimizing Machine Learning Performance
Master the art of applied machine learning with this comprehensive course. Learn to analyze changing data, identify unintended effects, and maintain your models. Gain the confidence to roll out and optimize machine learning projects in your business. Beginner-level Python programming and basic knowledge of linear algebra and statistics are recommended. Join Coursera and Amii for the final course in the Applied Machine Learning Specialization.
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).
