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.
About this Course
- 5 stars75.99%
- 4 stars18.56%
- 3 stars3.21%
- 2 stars0.99%
- 1 star1.23%
TOP REVIEWS FROM MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL
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!
Many useful information but need some more explanation, overall awesome
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
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.
About the Machine Learning: Algorithms in the Real World Specialization
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.
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