Back to Machine Learning Algorithms: Supervised Learning Tip to Tail
Learner Reviews & Feedback for Machine Learning Algorithms: Supervised Learning Tip to Tail by Alberta Machine Intelligence Institute
415 ratings
About the Course
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.
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 second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
Top reviews
M
Jun 22, 2020
Easy and engaging. But would loved it more if some more coding examples were given.
CW
Sep 29, 2020
Great course, easy to grasp the main idea of how to assess and tune the performance of question-answering machines learned by machine learning algorithms through data
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