최성철 교수 edwith
• 타입 MOOC 강좌
• 기간 상시 수강
• 시간 자유롭게 학습
• 수강 승인 방식 자동 승인

### 교수자 소개

• 최성철 교수

가천대학교 산업경영공학과 교수

### 강의계획

강의목록
1. CHAPTER 1 INTRO
1. Intro
1. 실습 프로그램 설치 안내
2. CHAPTER 2 Pythonic Code
1. Overview
1. Split & Join
1. List Comprehension
1. Enumerate & Zip
1. Lambda & MapReduce
1. Asterisk
1. <참고> Data Structure - Collections
1. 선형대수(Linear Algebra)
1. Linear algebra codes
1. Case Study - News Categorization
3. CHAPTER 3 Assignment
1. Basic Linear Algebra
1. Insert Operation
4. CHAPTER 4 Machine Learning Overview & An understanding of data
1. Python Ecosystem for ML
1. How to learn machine Learning
1. Representing a model
5. CHAPTER 5 Data handling
1. Numerical Python - Numpy
1. Pandas #1
1. Pandas #2
1. Visualization - matplotlib
1. Data Cleansing
1. Kaggle
6. CHAPTER 6 Assignment & Case Study
1. Assignment - Numpy Lab
1. Assignment - Lab Build Matrix
1. Case Study - Air Passengers
1. Case Study - Patent Data
7. CHAPTER 7 Linear Regression
1. Overview
1. Cost Function
1. Normal equation
1. Assignment - Normal equation
1. Linear Regression with GD & Implementation
1. Multivariate Linear Regression
1. Performance measure
1. Linear Regression w/sklearn
1. SGD implementation issues
1. Overfitting and Regularization
1. L2 - Regularization / Ridge
1. Sklearn lr
1. Polynomial Regression
1. Performance measure techniques
8. CHAPTER 8 Kaggle
1. Kaggle
9. CHAPTER 9 Logistic Regression
1. Overview
1. Sigmoid function
1. Cost function
10. CHAPTER 10 Classification Service
1. Classification Service

### 추가정보

[Publisher] 가천대학교 Teamlab 학부생연구원: 이현주