Patreon:
https://www.patreon.com/kfsoftPractical Machine Learning with scikit-learn (predictive data analysis)
1) Machine Learing concepts
- overfit / underfit, metrics
2) Regression
- demo 1: Boston housing dataset (train & evaluate multiple models)
- demo 2: Tips dataset (pre-processing)
Source:
https://github.com/learn10kYear/learn-pandas/blob/master/sklearn2/00:00 Introduction
01:59 Revision of last lesson
03:58 CONCEPT: ML concepts, data - X & y
08:35 Model parameters VS Hyperparameters
12:35 Generalization
14:34 Challenges 1: data
15:26 Challenges 2&3: overfit & underfit
20:38 Model evaluation: train_test_split()
22:25 Model evaluation: K-Fold cross-validation (CV)
24:50 Metrics - regression: MAE, MSE, RMSE
26:59 Metrics - classification: accuracy, confusion matrix, precision, recall
35:06 DEMO 1 - Boston housing dataset
35:55 Dataset descriptions
40:13 Analyze the dataset with pandas
42:30 Plot: boxplot, histogram, pairplot, jointplot
46:50 Step 1: Prepare X, y
48:11 Data Scaling: StandardScaler / MinMaxScaler
52:08 train_test_split()
54:22 Step 2 & 3: Training & Evaluation
54:36 Decision tree
55:11 Random forest (ensemble method)
57:48 List of models
01:00:00 Training (training set), and evaluation (testing set)
01:05:10 Cross Validation: cross_val_score()
01:09:24 Cross Validation: cross_validate()
01:11:16 Compute RMSE of different models with cross validation
01:13:07 Step 4: Prediction
01:16:13 DEMO 2 - Tips dataset (Mixed data)
01:17:36 Proprocessing intro - SimpleImputer, Encoder, pipeline, columnTransformer
01:19:54 Read dataset & plot charts
01:22:00 Step 1: SimpleImputer to remove NaN values
01:23:57 Text-to-Numeric: Categories without order - OneHotEncoder
01:25:08 Text-to-Numeric: Categories with order - OrdinalEncoder
01:26:33 Pandas.get_dummies(X)
01:28:58 Pipeline
01:31:44 ColumnTransformer
01:35:45 train_test_split
01:35:55 Step 2 & 3: Training & Evaluation
01:36:14 Plot tree method 1: plot_tree()
01:42:56 Plot tree method 2: export_graphviz()
01:46:15 Feature importances
01:47:26 Step 4: Prediction
01:50:23 Summary & conclusion
Python入門:第1課 - PyCharm + Data Types
https://youtu.be/s9toTBXQSPEPython入門:第2課 - Python containers (1): List, Tuple
https://youtu.be/7hm0zHgEGZ4Python入門:第3課 - Python containers (2): Dictionary & Set
https://youtu.be/7Jvfd6qFLzUPython入門:第4課 - If-Else, Looping, Try-except
https://youtu.be/sXdh5L5rcX0Python入門:第5課 - Function + File
https://youtu.be/rk8kU3no5NoPython入門:第6課 - Class and Object
https://youtu.be/HPb0Lg3FQfMPython入門:第7課 - URL, JSON, Sqlite
https://youtu.be/93lOZTxJtrsPython入門:第8課 - 用Flask進行Web開發
https://youtu.be/Z4CR3rwVkGcPython入門:第9課 - Flask + DB ORM
https://youtu.be/ZQoBdEH1zowPython入門:第10課 - Flask補充1
https://youtu.be/AC23QWvFNWIPython入門:第11課 - Flask補充2
https://youtu.be/-PkZ8sGhm-UPython入門:Project 2 - Password generator 密碼生成器
https://youtu.be/5miejVDO9_wPython初級:openpyxl - 讀寫 MS Excel 文件
https://youtu.be/tjcJV2fur5gPython初級:python-docx - 讀寫 MS Word 文件
https://youtu.be/PEKWb5R3sSUPython入門 - 數據科學 - Jupyter Lab & Notebook 安裝+入門教程
https://youtu.be/niWD8kxgpH0Python入門 - 數據科學 - Anaconda + PyCharm 安裝
https://youtu.be/H4ihRvtdY7MPython初級 - 數據科學 - Numpy入門
https://youtu.be/t7ygnafk760Python初級 - 數據科學 - Pandas入門
https://youtu.be/ZYjhM7J9eFQPython初級 - 數據科學 - Pandas時間 + 圖表
https://youtu.be/jrd8shHEVFQPython初級 - 數據科學 - Pandas類別 + 樣式
https://youtu.be/4ntwbAWnKbgDatabase初級:SQL入門
https://youtu.be/OtM74u3Fbw0Database初級:JOIN連接
https://youtu.be/tpDvgr7qHswDatabase初級:MongoDB入門
https://youtu.be/XTqW3oOt3PsPython初級 - 機器學習 - Scikit-learn 入門
https://youtu.be/3m8Bb01uNNEPython初級 - 機器學習 - Scikit-learn - Regression 回歸
https://youtu.be/QyYZT8o-f3UPython初級 - 機器學習 - Scikit-learn - Classification 分類
https://youtu.be/JKn0OoHSoRoPython初級 - 機器學習 - Scikit-learn - Clustering 聚類+降維
https://youtu.be/UgXyK-k-CgMhttps://kfsoft.infoAbout the Site 🌐
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