GitHub - ancestor9/AI-with-python: Python을 활용한 머신러닝 알고리즘

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Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
08_01_deep learning.ipynb		08_01_deep learning.ipynb
AI를 활용한 데이터 과학 심화과정.pdf		AI를 활용한 데이터 과학 심화과정.pdf
Gusian process understanding with scikit learn.ipynb		Gusian process understanding with scikit learn.ipynb
How to implement beyesian optimization.ipynb		How to implement beyesian optimization.ipynb
Time-related feature engineering.ipynb		Time-related feature engineering.ipynb
Working With Text Data.ipynb		Working With Text Data.ipynb
_Introduction to Machine learning .ipynb		_Introduction to Machine learning .ipynb
categorical data encoding.ipynb		categorical data encoding.ipynb
eigenvectors.ipynb		eigenvectors.ipynb
getting started.ipynb		getting started.ipynb
no-bs-guide-to-hyperparameter-tuning-with-optuna.ipynb		no-bs-guide-to-hyperparameter-tuning-with-optuna.ipynb
preliminary 1_non linear correlations.ipynb		preliminary 1_non linear correlations.ipynb
preliminary 2_market-basket-analysis.ipynb		preliminary 2_market-basket-analysis.ipynb
seminar_note_0_0_load dataset.ipynb		seminar_note_0_0_load dataset.ipynb
seminar_note_1_0_getting started.ipynb		seminar_note_1_0_getting started.ipynb
seminar_note_1_1_getting started.ipynb		seminar_note_1_1_getting started.ipynb
seminar_note_1_2_model evaluation.ipynb		seminar_note_1_2_model evaluation.ipynb
seminar_note_1_3_common pitfalls and recommended practices.ipynb		seminar_note_1_3_common pitfalls and recommended practices.ipynb
seminar_note_2_1_missing value_metric transform.ipynb		seminar_note_2_1_missing value_metric transform.ipynb
seminar_note_2_2_feature selection.ipynb		seminar_note_2_2_feature selection.ipynb
seminar_note_3_1_PCA.ipynb		seminar_note_3_1_PCA.ipynb
seminar_note_3_1_kernel PCA.ipynb		seminar_note_3_1_kernel PCA.ipynb
seminar_note_3_2_Nearest Neighbors.ipynb		seminar_note_3_2_Nearest Neighbors.ipynb
seminar_note_3_3_L1_L2_logistic regression.ipynb		seminar_note_3_3_L1_L2_logistic regression.ipynb
seminar_note_3_4_sgd.ipynb		seminar_note_3_4_sgd.ipynb
seminar_note_3_5_GNB with text mining.ipynb		seminar_note_3_5_GNB with text mining.ipynb
seminar_note_3_6_svc.ipynb		seminar_note_3_6_svc.ipynb
seminar_note_3_7_dt_ensemble.ipynb		seminar_note_3_7_dt_ensemble.ipynb
seminar_note_4_1_Kmeans_DBSCAN clustering.ipynb		seminar_note_4_1_Kmeans_DBSCAN clustering.ipynb
seminar_note_4_2_KMeans_prediction.ipynb		seminar_note_4_2_KMeans_prediction.ipynb
seminar_note_4_3_Kmeans with dtw.ipynb		seminar_note_4_3_Kmeans with dtw.ipynb
seminar_note_4_4_1D Kernel density estimation.ipynb		seminar_note_4_4_1D Kernel density estimation.ipynb
seminar_note_4_5_gmm clustering.ipynb		seminar_note_4_5_gmm clustering.ipynb
seminar_note_4_6_semi_supervised_learning.ipynb		seminar_note_4_6_semi_supervised_learning.ipynb
seminar_note_5_1_anomaly_detection_supervised-KNN.ipynb		seminar_note_5_1_anomaly_detection_supervised-KNN.ipynb
seminar_note_6_1_imbalance data.ipynb		seminar_note_6_1_imbalance data.ipynb
seminar_note_6_2_fine tuning_imbalance data.ipynb		seminar_note_6_2_fine tuning_imbalance data.ipynb
seminar_note_6_3_imbalance data handling.ipynb		seminar_note_6_3_imbalance data handling.ipynb
seminar_note_6_4_Tune Threshold explained with code.ipynb		seminar_note_6_4_Tune Threshold explained with code.ipynb
seminar_note_7_1_pycaret.ipynb		seminar_note_7_1_pycaret.ipynb
seminar_note_7_2_pycaret_anomaly.ipynb		seminar_note_7_2_pycaret_anomaly.ipynb
seminar_note_8_1_model agnostic interpretability (eXAI).ipynb		seminar_note_8_1_model agnostic interpretability (eXAI).ipynb
seminar_note_baseestimator.ipynb		seminar_note_baseestimator.ipynb
tuning with metrics.ipynb		tuning with metrics.ipynb
week_15_gaussian processes.ipynb		week_15_gaussian processes.ipynb

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