Python 初級 - 機器學習：scikit-learn - regression 回歸|AI|人工智能|數據分析|教學|廣東話

Patreon: https://www.patreon.com/kfsoft

Practical 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/s9toTBXQSPE
Python入門：第2課 - Python containers (1): List, Tuple https://youtu.be/7hm0zHgEGZ4
Python入門：第3課 - Python containers (2): Dictionary & Set https://youtu.be/7Jvfd6qFLzU
Python入門：第4課 - If-Else, Looping, Try-except https://youtu.be/sXdh5L5rcX0
Python入門：第5課 - Function + File https://youtu.be/rk8kU3no5No
Python入門：第6課 - Class and Object https://youtu.be/HPb0Lg3FQfM
Python入門：第7課 - URL, JSON, Sqlite https://youtu.be/93lOZTxJtrs
Python入門：第8課 - 用Flask進行Web開發 https://youtu.be/Z4CR3rwVkGc
Python入門：第9課 - Flask + DB ORM https://youtu.be/ZQoBdEH1zow
Python入門：第10課 - Flask補充1 https://youtu.be/AC23QWvFNWI
Python入門：第11課 - Flask補充2 https://youtu.be/-PkZ8sGhm-U
Python入門：Project 2 - Password generator 密碼生成器 https://youtu.be/5miejVDO9_w
Python初級：openpyxl - 讀寫 MS Excel 文件 https://youtu.be/tjcJV2fur5g
Python初級：python-docx - 讀寫 MS Word 文件 https://youtu.be/PEKWb5R3sSU

Python入門 - 數據科學 - Jupyter Lab & Notebook 安裝+入門教程 https://youtu.be/niWD8kxgpH0
Python入門 - 數據科學 - Anaconda + PyCharm 安裝 https://youtu.be/H4ihRvtdY7M
Python初級 - 數據科學 - Numpy入門 https://youtu.be/t7ygnafk760
Python初級 - 數據科學 - Pandas入門 https://youtu.be/ZYjhM7J9eFQ
Python初級 - 數據科學 - Pandas時間 + 圖表 https://youtu.be/jrd8shHEVFQ
Python初級 - 數據科學 - Pandas類別 + 樣式 https://youtu.be/4ntwbAWnKbg

Database初級：SQL入門 https://youtu.be/OtM74u3Fbw0
Database初級：JOIN連接 https://youtu.be/tpDvgr7qHsw
Database初級：MongoDB入門 https://youtu.be/XTqW3oOt3Ps

Python初級 - 機器學習 - Scikit-learn 入門 https://youtu.be/3m8Bb01uNNE
Python初級 - 機器學習 - Scikit-learn - Regression 回歸 https://youtu.be/QyYZT8o-f3U
Python初級 - 機器學習 - Scikit-learn - Classification 分類 https://youtu.be/JKn0OoHSoRo
Python初級 - 機器學習 - Scikit-learn - Clustering 聚類+降維 https://youtu.be/UgXyK-k-CgM

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