Supervised Machine Learning: Regression

This course is part of multiple programs. Learn more

Instructors: Mark J Grover +2 more

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Skills you'll gain

  •   Classification And Regression Tree (CART)
  •   Predictive Modeling
  •   Supervised Learning
  •   Regression Analysis
  •   Performance Metric
  •   Scikit Learn (Machine Learning Library)
  •   Statistical Analysis
  •   Machine Learning
  •   Statistical Modeling
  •   Feature Engineering
  • There are 6 modules in this course

    By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning  Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

    Data Splits and Polynomial Regression

    Cross Validation

    Bias Variance Trade off and Regularization Techniques: Ridge, LASSO, and Elastic Net

    Regularization Details

    Final Project

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