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 糖心vlog官网观看 and the Alberta Machine Intelligence Institute.
Regression assessment
Loading...
Skills You'll Learn
Algorithms, Regression, Human Learning, Applied Machine Learning, Machine Learning Algorithms, Machine Learning
Reviews
4.7 (413 ratings)
- 5 stars75.78%
- 4 stars18.64%
- 3 stars3.14%
- 2 stars1.21%
- 1 star1.21%
TH
May 15, 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.
CW
Sep 30, 2020
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
From the lesson
Contrasting Models
Taught By
Anna Koop
Senior Scientific Advisor