Embedded Files
L.1 overview
L.2 data types
L.3 mean
L.4 mode & median
L.5 standard deviation
L.6 variance
L.7 percentile
L.8 Data Distribution
L.9 normal data distribution
L.10 scatter plot
|-- Linear Regression --|
|-- Linear Regression --|
|------------------ Polynomial Regression -------------------|
|------------------ Polynomial Regression -------------------|
L.11 linear regression
L.12 R for relationship
L.13 polynomial regression
L.14 R-squared
L.15 predict future value
L.16 bad fit
|---------------- Multiple Regression ----------------|
|---------------- Multiple Regression ----------------|
L.17 Multiple Regression (+install sklearn)
(install module ต่างๆ ผ่าน pip โดยตรง โดยไปที่ shell และพิมพ์ว่า pip install scikit-learn scipy matplotlib numpy และ กด enter)
L.18 regression object
L.19 coefficient
L.20 Scaling
|------------------------------ Train / Test ------------------------------|
|------------------------------ Train / Test ------------------------------|
L.21. Create Data Set
L.22. split data set
L.23. fit data set
L.24. bring testing set
** ทางขวามือ >> เป็น tutorial พิเศษ สำหรับผู้ต้องการ ใช้ IDLE ในการทำเรื่อง Machine Learning โดยจะสอนวิธีติดตั้งโมดูลต่างๆ ที่จำเป็น นักเรียนทั่วไปข้ามไปได้จ้า **
install scikit-learn
|----------------------------------------- Decision Tree -----------------------------------------|
|----------------------------------------- Decision Tree -----------------------------------------|
L.25 read data.csv
L.26 map to numerical value
L.27 feature vs target columns
L.28 create decision tree
L.29 read result step1-2
L.30 read result step3-end
|-------------------------- Confusion Matrix --------------------------|
|-------------------------- Confusion Matrix --------------------------|
L.31 create confusion matrix
L.32 create confusion matrix 2
L.33 result explained
L.34 created metrics
|-------------------------------- Hirarchical Clustering --------------------------------|
|-------------------------------- Hirarchical Clustering --------------------------------|
L35 How it works?
L36 Plot data points
L37 Data Linkage
L38 Plot dendrogram
39 Plot dendogram 2
L40 Agglomerative Clustering
|----------- Logistic Regression -----------|
|----------- Logistic Regression -----------|
|----------- Grid Search -----------|
|----------- Grid Search -----------|
L41 How it works
L42 Predict result
L43 coefficient
L.44 hyperparameter and parameter
L.46 create a model
L.45 how it works
L.47 implement grid search
|---------------------------------- Categorical Data ----------------------------------|
|---------------------------------- Categorical Data ----------------------------------|
|---------------------------------- K - means ----------------------------------|
|---------------------------------- K - means ----------------------------------|
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