GitHub - thekimk/All-About-Time-Series-Analysis: [Advanced] Practical Statistical Inference and Machine/Deep Learning for Time Series Forecasting

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파이썬을 활용한 시계열 데이터 분석 강의자료 공간입니다.

강의링크: https://fastcampus.co.kr/data_online_pyt (종료)
공지사항:
- 동영상은 편집 과정에서 목차구성이나 내용에 변화 또는 오류가 있을 수 있으나, 강의자료는 항상 최신이며 정확합니다.
- 강의자료의 오탈자를 포함한 내용이슈가 있어서 언제든 문의주시면 최대한 빨리 업데이트 할 계획입니다.
- 21년도 하반기 오픈 이후, 지속적인 파이썬 업데이트로 인해 일부 실습이 미실행 될 수 있습니다.
- 고급 강의에 대한 요청이 많습니다만, 이미 예약된 강의/프로젝트 등으로 당분간 제공은 어려울 것 같습니다.
- 파이썬 사용법 튜토리얼은 크크인싸이트에 무료로 제공합니다.
- 저의 강의종류와 방향은 Curriculum-Guide에서 확인할 수 있습니다.
- 그 외 강의전반적인 질문이나 실습관련 이슈들의 자주 등장하는 질문들은 Question-and-Answer에서 확인할 수 있습니다.

Name		Name	Last commit message	Last commit date
Latest commit History 36 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Data		Data
Image		Image
Offline		Offline
Lecture1-1_Basic_RealDataAnalysis_KK.ipynb		Lecture1-1_Basic_RealDataAnalysis_KK.ipynb
Lecture1-2_Basic_MathStatistics_KK.ipynb		Lecture1-2_Basic_MathStatistics_KK.ipynb
Lecture1-3_Basic_BigDataSmallData_KK.ipynb		Lecture1-3_Basic_BigDataSmallData_KK.ipynb
Lecture1-4_Basic_LimitationVision_KK.ipynb		Lecture1-4_Basic_LimitationVision_KK.ipynb
Lecture2-1_DataAnalysis_LearningAlgorithm_KK.ipynb		Lecture2-1_DataAnalysis_LearningAlgorithm_KK.ipynb
Lecture2-2_DataAnalysis_BaseRegression_KK.ipynb		Lecture2-2_DataAnalysis_BaseRegression_KK.ipynb
Lecture2-4_DataAnalysis_BaseTimeSeries_KK.ipynb		Lecture2-4_DataAnalysis_BaseTimeSeries_KK.ipynb
Lecture3-1_MachineLearning_ErrorDecomposition_KK.ipynb		Lecture3-1_MachineLearning_ErrorDecomposition_KK.ipynb
Lecture3-2_MachineLearning_Regularization_KK.ipynb		Lecture3-2_MachineLearning_Regularization_KK.ipynb
Lecture3-3_MachineLearning_BaggingBoosting_KK.ipynb		Lecture3-3_MachineLearning_BaggingBoosting_KK.ipynb
Lecture3-4_StatisticalLearning_TimeSeries_LinearUnivariate_KK.ipynb		Lecture3-4_StatisticalLearning_TimeSeries_LinearUnivariate_KK.ipynb
Lecture3-5_StatisticalLearning_TimeSeries_LinearMultivariate_KK.ipynb		Lecture3-5_StatisticalLearning_TimeSeries_LinearMultivariate_KK.ipynb
Lecture3-6_StatisticalLearning_TimeSeries_NonLinearUnivariate_KK.ipynb		Lecture3-6_StatisticalLearning_TimeSeries_NonLinearUnivariate_KK.ipynb
Lecture4-1_DeepLearning_ArtificialIntelligent_KK.ipynb		Lecture4-1_DeepLearning_ArtificialIntelligent_KK.ipynb
Lecture4-3_DeepLearning_TimeSeriesAlgorithms_KK.ipynb		Lecture4-3_DeepLearning_TimeSeriesAlgorithms_KK.ipynb
Lecture_Mapping.xlsx		Lecture_Mapping.xlsx
Practice0_Installation_Program_KK.ipynb		Practice0_Installation_Program_KK.ipynb
Practice0_Installation_PythonJupyter_KK.pdf		Practice0_Installation_PythonJupyter_KK.pdf
Practice0_Installation_PythonJupyter_KK.pptx		Practice0_Installation_PythonJupyter_KK.pptx
Practice1-3_Basic_InputNumpy_KK.ipynb		Practice1-3_Basic_InputNumpy_KK.ipynb
Practice1-5_Basic_InputDataFrame_KK.ipynb		Practice1-5_Basic_InputDataFrame_KK.ipynb
Practice2-4_DataAnalysis_BaseTimeSeries_KK.ipynb		Practice2-4_DataAnalysis_BaseTimeSeries_KK.ipynb
Practice2-5_DataAnalysis_BaseTimeSeries_FeatureEngineering_KK.ipynb		Practice2-5_DataAnalysis_BaseTimeSeries_FeatureEngineering_KK.ipynb
Practice3-2_MachineLearning_RegularizationBaggingBoosting_TS_KK.ipynb		Practice3-2_MachineLearning_RegularizationBaggingBoosting_TS_KK.ipynb
Practice3-4_StatisticalLearning_TimeSeries_AutomationProphet_KK.ipynb		Practice3-4_StatisticalLearning_TimeSeries_AutomationProphet_KK.ipynb
Practice3-4_StatisticalLearning_TimeSeries_LinearUnivariate_KK.ipynb		Practice3-4_StatisticalLearning_TimeSeries_LinearUnivariate_KK.ipynb
Practice4-3_DeepLearning_TimeSeriesAlgorithms(keras)_KK.ipynb		Practice4-3_DeepLearning_TimeSeriesAlgorithms(keras)_KK.ipynb
README.md		README.md
[FastCampus] 커리큘럼_김경원박사.pptx		[FastCampus] 커리큘럼_김경원박사.pptx
module.py		module.py

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