Machine Learning Algorithms: Supervised Learning Tip to Tail
Learn and implement supervised learning techniques like decision trees, k-nearest neighbours, and support vector machines in real case studies. Gain skills to analyze business scenarios and handle data preparation steps. Basic Python programming and linear algebra knowledge required. Join Coursera’s Applied Machine Learning Specialization.
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 Coursera and the Alberta Machine Intelligence Institute.
