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Learning Objectives

After completing this module, you will understand:

  • What a hypothesis function is
  • What distinguishes parametric and non-parametric algorithms or models
  • How to numerically evaluate specific hypothesis functions
  • How the gradient descent algorithm works, and why it is needed
  • How to set and tune gradient descent learning rates
  • How to choose between Stochastic and Batch algorithms
  • How to evaluate Regression models using
      • Graphical methods
      • MAE
      • RMSE
      • Coefficient of Determination
  • How Regularization helps protect models from overfitting.

Course Curriculum

Course Tutorial
Introduction To Regression 00:05:00
Cost Function
Gradient Descent And Hypothesis Functions 00:48:30
Gradient Descent
Calculating Gradient Descent 00:45:30
Exercise: Visualizing Gradient Descent 00:00:00
Batch Gradient Descent
Tuning Batch Gradient 00:23:00
Stochastic Gradient Descent
Stochastic Vs Batch Gradient 00:06:00
Evaluating Regression Models 00:30:00
Exercise: Predicting Ozone Level Using Linear Regression 00:00:00
Regularization of Data 00:30:00
Exercise : Predicting Survival Using Logistic Regression 00:00:00
Instructor Led Exercise
Predict Real Estate Sales Price 00:10:00
Deliberate Practices
Exercise: Predicting MPG Ratings Using Various Linear Models 00:10:00
Regression Homework 00:20:00
Resources 00:05:00
Supplemental Exercises
Exercise: Predicting Wine Quality Using Linear Regression 00:10:00
Exercise (Python): Predicting Ozone Level Using Linear Regression 00:10:00
Exercise (Python): Predicting Ozone Level Using Regularized Linear Model 00:10:00
Exercise : Recognizing Handwritten Digits Using Linear Regression 00:10:00

Course Reviews


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  1. Test Review


    This is an awesome course!

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