Data for Machine Learning

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

Instructor: Anna Koop

Skills you'll gain

  •   Data Transformation
  •   Applied Machine Learning
  •   Data Processing
  •   Exploratory Data Analysis
  •   Data Cleansing
  •   Data Quality
  •   Unsupervised Learning
  •   Machine Learning
  •   Supervised Learning
  •   Feature Engineering
  •   Data Validation
  •   Machine Learning Algorithms
  •   Verification And Validation
  • There are 4 modules in this course

    Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, 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 third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

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