Machine Learning: Algorithms in the Real World Specialization
Machine Learning Real World Applications. Master techniques for implementing a machine learning project
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What you will learn
Clearly define an ML problem
Survey available data resources and identify potential ML applications
Prepare data for effective ML applications
Take a business need and turn it into a machine learning application
Skills you will gain
About this Specialization
We recommend a background in analytics, math (linear algebra, matrix multiplication), statistics and beginner level python programming.
We recommend a background in analytics, math (linear algebra, matrix multiplication), statistics and beginner level python programming.
There are 4 Courses in this Specialization
Introduction to Applied Machine Learning
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project.
Machine Learning Algorithms: Supervised Learning Tip to Tail
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.
Data for Machine Learning
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:
Optimizing Machine Learning Performance
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.
Offered by

Alberta Machine Intelligence Institute
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
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