Machine Learning Algorithms: Supervised Learning Tip to Tail

This course is part of Machine Learning: Algorithms in the Real World Specialization

Instructor: Anna Koop

Skills you'll gain

  •   Machine Learning
  •   Python Programming
  •   Jupyter
  •   Performance Analysis
  •   Data Processing
  •   Performance Metric
  •   Feature Engineering
  •   Scikit Learn (Machine Learning Library)
  •   Applied Machine Learning
  •   Machine Learning Algorithms
  •   Classification And Regression Tree (CART)
  •   Regression Analysis
  •   Supervised Learning
  •   Business Solutions

    • There are 4 modules in this course

    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.

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