MACHINE LEARNING FUNDAMENTALS

Basic (1-10)

1 overview

05:02
2 data types

03:10
3 mean

07:09
4 mode & median

17:06
5 standard deviation

06:45
6 variance

08:59
7 percentile

09:14
8 Data Distribution

13:18
9 normal data distribution

07:55
10 scatter plot

14:04

Linear Regression (11-12)

Polynomial Regression (13-16)

Multiple Regression (17-20)

Train / Test (21-24)

Install scikit-learn documentation (พิเศษสำหรับคนที่จะใช้ IDLE)
เป็น tutorial พิเศษ สำหรับผู้ต้องการ ใช้ IDLE ในการทำเรื่อง Machine Learning โดยจะสอนวิธีติดตั้งโมดูลต่างๆ ที่จำเป็น นักเรียนทั่วไปข้ามไปได้จ้า **

Decision Tree (25-30)

Confusion Matrix (31-34)

Hirarchical Clustering (35-40)

Logistic Regression (41-43)

Grid Search (44-47)

Categorical Data (48-50)

K – means (51-52)

Bootstrap Aggregatioin

Cross Validation

AUC – ROC Curve

K-nearest neighbors

Print Certificate

Course Content

Basic (1-10)

1 overview

2 data types

3 mean

4 mode & median

5 standard deviation

6 variance

7 percentile

8 Data Distribution

9 normal data distribution

10 scatter plot

Linear Regression (11-12)

11 linear regression

12 R for relationship

Polynomial Regression (13-16)

13 polynomial regression

14 R-squared

15 predict future value

16 bad fit

Multiple Regression (17-20)

17 Multiple Regression (+install sklearn)

18 regression object

19 coefficient

20 Scaling

Train / Test (21-24)

21 Create Data Set

22 split data to train test

23 fit data set

24 bring testing set

install pip & scikit learn

Decision Tree (25-30)

25 read data.csv decision tree slides

26 map to numerical value

27 feature vs target columns

28 create decision tree

29 read result step1-2

30 read result step3-end

Confusion Matrix (31-34)

31 create confusion matrix

32 create confusion matrix 2

33 result explained 1

34 created metrics

Hirarchical Clustering (35-40)

35 how it works

36 Plot data points

37 data linkage

38 plot dendrogram

39 plot dendrogram 2

40 agglomerative clustering

Logistic Regression (41-43)

41 How it works

42 Predict result

43 coefficient

Grid Search (44-47)

44 hyperparameter and parameter

45 how it works

46 create a model

47 implement grid search

Categorical Data (48-50)

48 One Hot Encoding

49 predict CO2

50 dummifying

K – means (51-52)

51 how does it work

52 train K means

Bootstrap Aggregatioin

Cross Validation

AUC – ROC Curve

K-nearest neighbors

Print Certificate

Fill and print certificate

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