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
About this Course
Syllabus - What you will learn from this course
Classification using Decision Trees and k-NN
Functions for Fun and Profit
Regression for Classification: Support Vector Machines
- 5 stars76.16%
- 4 stars18.42%
- 3 stars3.19%
- 2 stars0.98%
- 1 star1.22%
TOP REVIEWS FROM MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL
really good, wish it had covered random forest and decision trees and other supervised models as well.
I found the course to be enough detailed to get clarity on the basic concepts of Supervised learning algorithms. I hope to apply the learning from the course in work!
The whole specialization is extremely useful for people starting in ML. Highly recommended!
The explanation of the topics are easy to understand due to the dynamics of theory, practical exercises and quizzes.
About the Machine Learning: Algorithms in the Real World Specialization
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