머신러닝/딥러닝을 위한 Python

강의개요

본 강의는 머신러닝, 딥러닝을 배우기 위해 기본적으로 이해해야하는 Python을 다룹니다. 빠른 시간 내에 Python 기초 문법을 복습하고 머신러닝, 딥러닝의 근간을 이루는 Numpy, Pandas와 친숙해지고 싶은 분에게 추천합니다. 참고 - 머신러닝/딥러닝을 위한 Python

강의정보

• 강좌명: 머신러닝/딥러닝을 위한 Python
• 강의자명: 가천대학교 산업경영공학과 최성철 교수 ([email protected], Director of TEAMLAB)
• Email: [email protected]

강의구성

Chapter 0 - Environment setup

• 파이썬 설치 - 강의영상
• Atom 설치
  • windows - 강의영상, 설치문서
  • Mac - 강의영상, 설치문서
• Python Ecosystem for Machine Learning - 강의영상

Package installation

conda create -n ml_python python=3.5
conda install numpy seaborn scikit-learn jupyter
conda install nltk gensim matplotlib

Chapter 1 - Pythonic Code

• Pythonic Code Overview
• Split & Join
• List Comprehension
• Enumerate & Zip
• Map & Reduce
• Asterisk
• Lab: Simple Linear algebra concepts
• Lab: Simple Linear algebra codes
• Assignment: Linear algebra with pythonic code
• Assignment: 연산자 끼워넣기
• Assignment: 톱니바퀴

Chapter 2 - Numpy section

• Numpy overview
• ndarray
• Handling shape
• Indexing & Slicing
• Creation functions
• Opertaion functions
• Array operations
• Comparisons
• Boolean & fancy Index
• Numpy data i/o
• Assignment: Numpy in a nutshell

Chapter 3 - Pandas section

• Pandas overview
• Series
• DataFrame
• Selection & Drop
• Dataframe operations
• lambda, map apply
• Pandas builit-in functions
• Lab Assignment: Build a matrix
• Groupby I
• Groupby II
• Casestudy
• Pivot table & Crosstab
• Merg & Concat
• Database connection & Persistance

Chapter 4 - OOP section

• Objective oriented programming overview
• Objects in Python
• Lab: Note and Notebook
• OOP characteristics
• Decorators, Static And Class Methods
• Abstract Classes

Chapter 5 - Linear regression

• Linear regression overview
• Cost functions
• Linear Equality
• Gradient descent approach
• Linear regression wtih gradient descent
• Linear regression wtih Numpy
• Multivariate linear regression models
• Multivariate linear regression with NumPy
• • Regularization - L1 and L2
• Implementation of generalization with NumPy
• Linear regression with sklearn

Chapter 6 - Logistic regression

• Logistic regression overview
• Sigmoid function
• Cost function
• Logistic regression implementation with Numpy
• Maximum Likelihood estimation
• Regularization problems
• Logistic regresion with sklearn
• Softmax fucntion for Multi-class classification
• Cross entropy loss function
• Softmax Logistic Regression
• Performance measures for classification

References

• K-MOOC: 데이터 과학을 위한 파이썬 입문
• Operation Research with Python Programming