Zen: Online Learning

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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 Tutorial
Introduction
	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
Evaluation
	Evaluating Regression Models	00:30:00
	Exercise: Predicting Ozone Level Using Linear Regression	00:00:00
Regularization
	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
Homework
	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

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