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최성철 교수
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. Gradient Descent
    1. Linear Regression with GD & Implementation
    1. Multivariate Linear Regression
    1. Performance measure
    1. Linear Regression w/sklearn
    1. Stochastic Gradient Descent
    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