Update readme

Author: dongminlee94

README.md

@@ -1,210 +1,26 @@
-# Python for Machine Learning
+# Getting started
 
-## Intro
+## Requirements
 
-- Python을 계속 사용하면서 Data를 다루는 데에 있어 기초가 부족하다는 것을 느꼈기 때문에 Machine Learning과 관련된 Python Code들을 정리하고자 한다.
-- Numpy, Pandas, Matplotlib 등 Data Handling에 보다 더 집중하여 공부할 것이다.
+This repository is implemented and verified on python 3.8.12.
 
-## Contents
-
-### 01. [Pythonic Code](https://github.com/dongminleeai/Python-for-ML/tree/master/01.%20Pythonic%20Code)
-
-1. [Lecture](https://github.com/dongminleeai/Python-for-ML/tree/master/01.%20Pythonic%20Code/Lecture)
-
-    - [Pythonic Code](https://github.com/dongminleeai/Python-for-ML/blob/master/01.%20Pythonic%20Code/Lecture/01-1.%20Pythonic%20Code.ipynb)
-        - Overview
-        - Split & Join
-        - List Comprehension
-        - Enumerate & Zip
-        - Lambda & MapReduce
-        - Asterisk
-        - Data Structure - Collections
-        - Linear algebra codes
-
-    - [News Categorization](https://github.com/dongminleeai/Python-for-ML/blob/master/01.%20Pythonic%20Code/Lecture/01-2.%20News%20Categorization.ipynb)
-        - One-hot Encoding
-        - Bag of words
-        - Distance measure
-            - Euclidian distance
-            - Cosine distance
-        - Corpus 만들기 + 단어별 index 생성하기
-        - 문서별로 Bag of words vector 생성
-        - Cosine similarity로 비교하기
-        - Top n similarity news 뽑아내기
-        - Accuracy 측정하기
-
-2. [Assignment](https://github.com/dongminleeai/Python-for-ML/tree/master/01.%20Pythonic%20Code/Assignment/1.%20Pythonic%20Code%20Lab)
-
-    - [Pythonic Code Lab](https://github.com/dongminleeai/Python-for-ML/tree/master/01.%20Pythonic%20Code/Assignment/1.%20Pythonic%20Code%20Lab)
-
-        - vector_size_check
-        - vector_addition
-        - vector_subtraction
-        - scalar_vector_product
-        - matrix_size_check
-        - is_matrix_equal
-        - matrix_addition
-        - matrix_subtraction
-        - matrix_transpose
-        - scalar_matrix_product
-        - is_product_availability_matrix
-        - matrix_product
+## Installation
 
-<br>
+Clone the repository and run one of the following commands.
 
-### 02. [Data Handling](https://github.com/dongminleeai/Python-for-ML/tree/master/02.%20Data%20Handling)
+```shell
+$ make setup
+```
 
-1. [Lecture](https://github.com/dongminleeai/Python-for-ML/tree/master/02.%20Data%20Handling/Lecture)
-
-    - [Overview](https://github.com/dongminleeai/Python-for-ML/blob/master/02.%20Data%20Handling/Lecture/02-1.%20Overview.ipynb)
-
-    - [Numpy](https://github.com/dongminleeai/Python-for-ML/blob/master/02.%20Data%20Handling/Lecture/02-2.%20Numpy.ipynb)
-        - Numpy이란?
-        - Numpy 특징
-        - Array creation
-            - shape
-            - dtype
-        - Handling shape
-            - reshape
-            - flatten
-        - Indexing & Sclicing
-        - Creation function
-            - arange
-            - ones, zeros & empty
-            - something_like
-            - identity
-            - eye
-            - diag
-            - random sampling
-        - Operation functions
-            - sum
-            - axis
-            - mean & std
-            - Mathematical functions
-            - concatenate
-        - Array operations
-            - Operations b/t arrays
-            - Element-wise operations
-            - Dot product
-            - transpose
-            - broadcasting
-            - Numpy performance
-        - Comparisons
-            - All & Any
-            - Comparison operation
-            - np.where
-            - argmax & argmin
-        - Boolean & Fancy index
-            - boolean index
-            - fancy index
-        - Numpy data i/o
-            - loadtxt & savetxt
-            - numpy object - npy
-
-    - [Pandas](https://github.com/dongminleeai/Python-for-ML/blob/master/02.%20Data%20Handling/Lecture/02-3.%20Pandas.ipynb)
-        - Pandas란?
-        - 데이터 로딩
-        - Pandas의 구성
-        - Series
-        - Dataframe
-        - Selection & Drop
-            - Selection with column names
-            - Selection with index number
-            - Series selection
-            - Index 변경
-            - Basic, loc, iloc selection
-            - index 재설정
-            - Data drop
-        - Dataframe Operations
-            - Series operation
-            - Dataframe operation
-            - Series + Dataframe
-        - Lambda, Map, Apply
-            - Map for series
-            - Replace function
-            - Apply for dataframe
-            - Applymap for dataframe
-        - Pandas Built-in functions
-            - describe
-            - unique
-            - label str -> index 값으로 변환
-            - sum
-            - isnull
-            - sort_values
-        - Groupby
-            - Groupby 사용법
-            - Hierarchical index
-                - unstack()
-                - swaplevel()
-            - gropued
-            - aggregation
-            - transformation
-            - filter
-        - Case study (phone_data.csv)
-        - Pivot table & Crosstab
-        - Merge & Concat
-            - Merge
-                - Inner join
-                - Left join
-                - Right join
-                - Full(outer) join
-            - Concat
-        - Persistence
-            - DB persistence
-            - XLS persistence
-            - Pickle persistence
-
-    - [Matplotlib](https://github.com/dongminleeai/Python-for-ML/blob/master/02.%20Data%20Handling/Lecture/02-4.%20Matplotlib.ipynb)
-        - Matplotlib란?
-        - 기본 사용법
-            - Matplotlib
-            - Set color
-            - Set linstyle
-            - Set title
-            - Set legend
-            - Set grid & xylim
-        - Matplotlib Graph
-            - Scatter
-            - Bar chart
-            - Histogram
-            - Boxplot
-        - Matplotlib with pandas
-
-    - [Data Cleaning](https://github.com/dongminleeai/Python-for-ML/blob/master/02.%20Data%20Handling/Lecture/02-5.%20Data%20Cleansing.ipynb)
-        - Data problems
-            - Data quality problems
-            - Data preprocessing issues
-        - Missing Values
-            - 데이터가 없을 때 할 수 있는 전략
-            - Data drop
-            - Data Fill
-        - Category data
-            - One Hot Encoding
-            - Data binning
-        - Feature scaling
-            - Min-Max Normalization
-            - Z-Score Normalization
-
-    - [Kaggle - Titanic](https://github.com/dongminleeai/Python-for-ML/blob/master/02.%20Data%20Handling/Lecture/02-6.%20Kaggle%20-%20Titanic.ipynb)
-        - Titanic이란?
-        - Load dataset
-        - Data preproecessing
-            - Check null values
-            - Drop Columns
-            - Add to null values
-            - One-Hot Encoding Columns
-        - Build Model
-
-2. [Assignment](https://github.com/dongminleeai/Python-for-ML/tree/master/02.%20Data%20Handling/Assignment)
+## Contents
 
-    - [Numpy Lab](https://github.com/dongminleeai/Python-for-ML/tree/master/02.%20Data%20Handling/Assignment/1.%20Numpy%20Lab)
+If you want to see jupyter notebook files better, please see them in [nbviewer](https://nbviewer.org/github/dongminlee94/Python-for-ML).
 
-        - n_size_ndarray_creation
-        - zero_or_one_or_empty_ndarray
-        - change_shape_of_ndarray
-        - concat_ndarray
-        - normalize_ndarray
-        - save_ndarray
-        - boolean_index
-        - find_nearest_value
-        - get_n_largest_values
+1. [Pythonic code]()
+2. [News categorization]()
+3. [Overview]()
+4. [Numpy]()
+5. [Pandas]()
+6. [Matplotlib]()
+7. [Data cleansing]()
+8. [Kaggle - Titanic]()