1. The document discusses supervised learning techniques for regression. It covers different types of regression models like linear, polynomial, lasso, ridge regression and decision tree regression. 2. Regression algorithms can be used for applications like sales forecasting, satisfaction analysis, price estimation and more. Common regression algorithms mentioned include linear regression, decision tree regression and neural network regression. 3. The document also discusses how to fit regression models to data, estimate parameters, and make predictions using simple linear regression and multiple linear regression.

1. Lesson 11 :
Supervised Learning - Regression

2. Intro to Supervised Learning
Clasification Algorithm
(Logistic Regression, Decision Tree, RF, SVM, Naïve Bayes)
Model Evaluation
Materi

3. Supervised Learning
● Supervised Learning dilatih menggunakan contoh
berlabel , seperti input di mana output yang
diinginkan diketahui.
● Misalnya, segmen teks dapat memiliki label kategori,
seperti:
○ Spam vs. Email
○ Ulasan Film

4. Type of Supervised Learning

5. What is Regression

6. Types of Regression Models

7. Types of Regression Models (cont.)

8. Application of Regression
● Sales forecasting
● Satisfaction Analysis
● Price Estimation
● Employment Income
● etc.

9. Regression Algorithm
● Ordinal Regression
● Poisson Regression
● Fast Forest Quantile Regression
● Linear, Polynomial, Lasso, Stepwise, Ridge Regression
● Bayesian Linear Regression
● Neural Network Regression
● Decision Forest Regression
● Boosted Decision Tree Regression
● KNN (K-nearest Neighbors

10. Simple Linear Model

11. How to find the best fit?

12. Estimating Parameters

13. Prediction with Linear Regression

14. Estimating Multiple Linear Regression Parameters

15. Making Prediction with multiple Linear Regression

16. Q&A – On Multiple Linear Regeression