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.
This course is part of the Machine Learning: Algorithms in the Real World Specialization
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About this Course
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Course 2 of 4 in the
Approx. 9 hours to complete
English
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 4 in the
Approx. 9 hours to complete
English
Offered by
Syllabus - What you will learn from this course
4 hours to complete
Classification using Decision Trees and k-NN
4 hours to complete
8 videos (Total 46 min), 4 readings, 2 quizzes
2 hours to complete
Functions for Fun and Profit
2 hours to complete
9 videos (Total 62 min), 1 reading, 4 quizzes
3 hours to complete
Regression for Classification: Support Vector Machines
3 hours to complete
6 videos (Total 34 min), 1 reading, 2 quizzes
1 hour to complete
Contrasting Models
1 hour to complete
8 videos (Total 46 min), 1 reading, 1 quiz
Reviews
- 5 stars76.04%
- 4 stars18.51%
- 3 stars3.20%
- 2 stars0.98%
- 1 star1.23%
TOP REVIEWS FROM MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL
by THMay 14, 2022
This is an excellent course which goes into some depth on the different ML models and underlying complexity but it avoids getting bogged down into the details too much.
by MJOct 29, 2019
Great course! I received so much useful information from AMII.
by VKOct 27, 2020
Learn some valuable insights on scikit-learn capabitlity through the labs
by BSOct 4, 2020
Excellent instruction. One of the best in ML. Could use a bit more python though.
About the Machine Learning: Algorithms in the Real World Specialization

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