Machine Learning Algorithms: Supervised Learning Tip to Tail

This specialization 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 specialization will set you up to define, train, and maintain a successful machine learning application.After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results,

( 4 Reviews )

Created by: Anna Koop

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Overall Score : 90 / 100

Course Description

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.

Time: 12h

Instructor Details

Anna Koop

4.5 Rating
4 Reviews

Anna is Senior Scientific Advisor at the Alberta Machine Intelligence Institute (Amii), working to nurture productive relationships between industry and academia. Anna, whose research mainly focused on reinforcement learning, received her Master's in Computing Science under the supervision of Dr. Richard Sutton, one of the field's pioneers, and she is currently a PhD candidate working to develop algorithms for real-time learning in dynamic environments. Passionate about making science accessible for all, Anna has developed and taught a wide range of computing science classes through the University of Alberta.

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4.5

4 total reviews

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By Miguel A S M on 15-Oct-19

Excellent.Teach you practical stuff that other courses don't.

By M J on 30-Oct-19

Great course! I received so much useful information from AMII.

By Cheng H Z on 10-Oct-19

Explained things clearly

By Luiz C on 11-Sep-19

Had higher expectations. Concepts not well and clearly explained. Notebooks bugged (we are actually warned about it), but even so not so interesting. Plan of the Course not so rational: why include the one section about model parameters on its own, rather than for each model.I give it a 3 as the Instructor is smily and engaging, but it's a 2.5 mark (I have done another ML MOOC on another concurrent platform about the same topic, and the quality was much higher)