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 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
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.
Great course! I received so much useful information from AMII.
Learn some valuable insights on scikit-learn capabitlity through the labs
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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