• No products in the cart.

### 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 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

## 5

5
1 ratings
• 5 stars1
• 4 stars0
• 3 stars0
• 2 stars0
• 1 stars0
1. ### Test Review

5

This is an awesome course!

5487 STUDENTS ENROLLED