本筆記中使用的程式語言為python，課程為：机器学习 A-Z (Machine Learning A-Z in Chinese)

程式編輯器：spyder

python版本：3.6

如何安裝及使用packege內的程式

EX1：

import Numpy  #導入程式庫
X1, X2 = Numpy .meshgrid.....

EX2: 

import Numpy as np
X1, X2 = np .meshgrid.....  #之後使用就可以簡化

EX3:

from sklearn.ensemble import RandomForestClassifier as  RFC   #直接導入sklean model 內RandomForestClassifier

classifier = RFC(......

NumPy         多維數列與矩陣運算用，最常搭配matplotlib使用

np.arange(start = X_set[:, 0].min() – 1, stop = X_set[:, 0].max() + 1, step = 0.01)

Matplotlib.pyplot   視覺化套件，只有一張圖的話使用全域 pyplot 很方便 

plt.xlim(X1.min(), X1.max())

plt.scatter()plt.title(“)plt.xlabel(“)plt.ylabel(“)plt.show

pandas  數據分析用

pd.read_csv(‘XXX.xsv’)

os   文件和目錄操作，學習中主要用在自動跳到工作路徑用，避免多次手動安裝路徑

os.chdir(“D:\Machine Learning A-Z Chinese Template Folder\Part 3 – Classification\Section 10 – Logistic Regression")

random

random.betavariante( thida1, 1-thida1)

sklearn    為scikit-learn縮寫，數據處理相關模組，常用程式庫如下：

from sklearn.impute import SimpleImputer #遺失數據處理

imputer = SimpleImputer(missing_values =np.nan, strategy= ‘mean’)

imputer.fit(X[:, 1:3])

X[:, 1:3] = imputer.transform(X[:, 1:3])

from sklearn.model_selection import train_test_split  #自動將數據及分為訓練集及測試集

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.25, random_state = 0 )

from sklearn.preprocessing import StandardScaler   #將數據標準化

sc = StandardScaler()

X_train = sc.fit_transform(X_train)  #fit 會找出平均值與標準差並標準化

X_test = sc.transform(X_test) #沒有fit 會直接用上面有fit過得到的平均值與標準差進行標準化

from sklearn.preprocessing import LabelEncoder, OneHotEncoder #將文字分類轉變為數字

from sklearn.linear_model import LinearRegression #線性回歸

from sklearn.preprocessing import PolynomialFeatures #多項式回歸

from sklearn.linear_model import LogisticRegression  #邏輯回歸

classifier = LogisticRegression(random_state = 0)  

classifier.fit(X_train, Y_train)     

Y_pred = classifier.predict(X_test)

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier as rfc

import sklearn.cluster as cl  

kmeans = cl.KMeans(n_clusters = i , max_i…..

import apyori as ap

rules = ap.apriori(transations, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2) 

import  re  #幫助清理語言資料