INDEX

AI부트캠프

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Section 01 데이터 분석

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Sprint 1 Data Preprocess & EDA

	Sprint 1	세부내용
Day 1	EDA	1. 데이터셋 불러오기 2. EDA 3. 전처리
Day 2	Feature Engineering	1. Feature Engineering 2. String replace 3. Apply
Day 3	Data Manipulation	1. 데이터 합치기 2. tidy 데이터
Day 4	Basic Derivative	1. 미분 2. 편미분 3. 합성함수 4. 경사하강법
Day 5	Sprint Challenge 1	Sprint Challenge

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Sprint 2 Statistics

	Sprint 2	세부내용
Day 1	Hypothesis Test	1. 기술 통계치 2. 추리 통계치 3. 가설 검정( T-test )
Day 2	Chi-Square Test	1. T-test 2. Type of error 3. chi-square tests 4. 자유도
Day 3	Confidence Interval	1. ANOVA 2. 큰 수의 법칙 3. 중심극한정리 4. 신뢰도
Day 4	Bayesian	1. 베이지안 통계 2. 베이지안 이론
Day 5	Sprint Challenge 2	Sprint Challenge

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Sprint 3 Linear Algebra

	Sprint 3	세부내용
Day 1	Vector / Matrix	1. Data Structure 2. Matrix 3. Norms 4. Errors 5. Inverse
Day 2	Linear Algebra +	1. 분산 2. 표준편차 3. 공분산 4. 상관계수 5. 직교 6. span 7. basis 8. rank
Day 3	High-Dimensional Data	1. 고유벡터/고유값 2. PCA
Day 4	Clustering	1. Scree Plot 2. K-means Clustering
Day 5	Sprint Challenge 3	Sprint Challenge

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Section 02 Machine Learning

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Sprint 1 Linear Models

	Sprint 1	세부내용
Day 1	Simple Regression	1. 기준모델 2. 예측모델 3. 단순선형회귀
Day 2	Multiple Regression	1. train/test 2. 다중선형회귀모델 3. 회귀계수 4. 평가지표 5. 과/소적합
Day 3	Ridge Regression	1. One-hot Encoding 2. Feature Selection 3. Ridge Regression
Day 4	Logistic Regression	1. train, val, test 2. Classification 3. Logistic Regression

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Sprint 2 Tree Based Model

	Sprint 2	세부내용
Day 1	Decision Trees	1. Cardinality 2. Pipeline 3. Decision Tree
Day 2	Random Forests	1. Random Forest 2. Bagging 3. Ordinal Encoder
Day 3	Evaluation Metrics for Classification	1. Confusion Matrix 2. Trhesholds 3. ROC Curve
Day 4	Model Selection	1. CV 2. 하이퍼파라미터 3. 검증곡선 4. RandomizedSearchCV

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Sprint 3 Applied Predictive Modeling

	Sprint 3	세부내용
Day 1	Choose Your ML Problems	1. Data leakage 2. 불균형 클래스
Day 2	Data Wrangling	1. Data Wrangling
Day 3	Feature Importances	1. Permutation Importances 2. Boosting
Day 4	Interpreting ML Model	1. PDP 2. SHAP

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Section 03 Data Engineering

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Section 04 Deep Learning

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Section 05 Algorithm

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