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å н å ϴ ϰ ϰ Ư¡Դϴ. , н ̷ Ű ٴ н̶ ִ ɷ Ű ʵ Ȱ ֵ Ͽϴ. å κп 켱 н ̿ м 信 մϴ. Ŀ ȸͺм ̿ м, Ʈ ̿ м, ΰŰ ̿ м, SVM ̿ м, ؽƮ м, K-means K-nearest neighbor ̿ м  մϴ. پ Ư¡ ͸ ܼȭ ϴ ִ ּ м(PCA)  մϴ.


CHAPTER 1
1.1 м
1.2 м ʿ
1.3 å  
 
CHAPTER 2 н ̿ ͺм
2.1 н(Machine Learning) Ұ
2.1.1 н
2.1.2 н
2.1.3 н Ȱ м
2.2
2.2.1
2.3 ͸ ǽ
2.3.1 Scikit Learn Toy Data ǽ 
 
CHAPTER 3 ȸͺм ̿ м
3.1 ϼȸͺм
3.1.1 ϼȸͺм̶?
3.1.2 ϼȸ͸ Ұ
3.1.3 յ
3.1.4
3.1.5 ϼȸͺм ǽ –Basic 1
3.1.6 ϼȸͺм ǽ –Basic 2
3.2 ߼ȸͺм
3.2.1 ߼ȸͺм̶?
3.2.2 յ
3.2.3 ߼ȸͺм ǽ –Basic 1
3.2.4 ߼ȸͺм ǽ –Basic 2 
 
CHAPTER 4 Ʈ ̿ м
4.1 ǻ Ʈ ̿ м
4.1.1 ǻ Ʈ(Decision Tree)?
4.1.2 ǻ Ʈ
4.1.3 Decision Tree м
4.1.4 ̿ ǻ Ʈ
4.1.5 ǻ Ʈ и (Split Criterion)
4.1.6 ̿ ǻ Ʈ ǽ
4.2 Ʈ(Random Forest) ̿ м
4.2.1 Ʈ Ұ
4.2.2 Ʈ ̷
4.2.3 Iris ͸ ̿ Ʈ  
 
CHAPTER 5 ΰŰ ̿ ͺм
5.1 ΰŰ(Artificial Neural Network : ANN)
5.1.1 ΰŰ
5.1.2 ΰŰ –ϰŰ
5.2 ΰŰ ̿ м
5.2.1 ΰŰ –Ű
5.2.2 ̿ ΰŰ ǽ 
 
CHAPTER 6 Support Vector Machine
6.1 Support Vector Machine (SVM)
6.1.1 SVM
6.2 Support Vector Machine ǽ
6.2.1 Python package ε
6.2.2 Iris data set ε
6.2.3 Iris data set Ȯ
6.2.4 н
6.2.5 ðȭ ó
6.2.6 ðȭ
6.3 SVM Parameter ϴ ǽ 
 
CHAPTER 7 Naive Bayes
7.1 Naive Bayes
7.1.1 Naive Bayes?
7.1.2 (Bayes theorem)
7.1.3 Ǻ Ȯ(Conditional Probability)
7.1.4 ö (Laplace Smoothing)
7.1.5 Log ȯ
7.2 ̿ Naive Bayes
7.3 ̿ Naive Bayes Python ڵ ǽ
7.3.1 ʿ package ε
7.3.2 ε
7.3.3 ó
7.3.4 и
7.3.5 Train, Test Set
7.3.6 ̺
7.3.7 Ŭ
7.3.8 Ŭ Ȯ
7.3.9 з  
 
CHAPTER 8 ؽƮ м
8.1 ؽƮ м
8.1.1 ؽƮ м
8.1.2 ūȭ
8.1.3 
8.1.4 ¼ м
8.1.5
8.1.6 з
8.1.7 м
8.2 ؽƮ м
8.2.1 ؽƮ м
8.2.2
8.2.3 ؽƮ ó
8.2.4 Word Cloud
8.2.5 Ư¡
8.2.6 з
 
CHAPTER 9 ѱ ؽƮ з
9.1 ѱ ؽƮ з
9.1.1 ѱ ؽƮ з
9.1.2 ° Ư¡
9.1.3 з 
 
CHAPTER 10 Ÿ н ̿ м
10.1 K-means
10.1.1 K-means ˰̶?
10.1.2 K-means Ŭ͸
10.1.3 Scikit-learn(Sklearn) Ű Ұ
10.1.4 K-means ǽ
10.2 K-Nearest Neighbors(KNN)
10.2.1 K-Nearest Neighbors (KNN) ˰̶?
10.2.2 Scikit-learn(Sklearn) Ű Ұ
10.2.3 KNN ǽ 
 
CHAPTER 11 PCA LDA
11.1
11.1.1 (Dimensionality)
11.1.2 (Curse of Dimensionality)
11.1.3
11.2 PCA
11.2.1 PCA
11.2.2 (Eigenvectors) (Eigenvalues)
11.2.3 PCA 籸
11.3 LDA
11.3.1 LDA
11.4 ͸ ǽ
11.4.1 ʿ Ű import
11.4.2 Ȯ
11.4.3 PCA
11.4.4 LDA
11.4.5 , PCA, LDA ðȭ


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