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Ken Cen

課程內容包括Python,機械學習,及各種實際運用的實例。本課程將從零基礎開始學習Python語言,了解Python的基本語言,學習Machine Learning的數據處理庫Numpy,Pandas,數據可視化工具庫Matplotlib等。然後,開始了解,Machine Learning 中的Linear Regression,Classification,Clustering,以及對應的演算法,和各種或然率概念。

人工智能是現在最流行和熱門的話題,人才需求也非常巨大,然而,要學習了解這門科學,卻非常困難,原因在於這是一個集中多學科,包括Pyhon語言學習,對應Numpy, Pandas等數據處理,還有數學中,統籌學,或然率概念,方程式等複雜概念。

課程會回歸一個無任何編程經驗的學員角度,一步一步了解實踐這門科學,這將是一個奇妙學習的旅程,希望能與您結伴同行!

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What's inside

Learning objectives

  • 了解python關於數據科學方面的知識
  • 了解python中numpy , pandas, matplotlib的使用方式
  • 了解linear regression,classification以及clustering的原理以及實現方式
  • 了解如何在多個特徵點上作出正確的選擇,並在python上實現

Syllabus

介紹
課程介紹
課程禮物
Python基礎
Read more

編寫第一段python代碼

JupyterNoteBook介紹

Variable變量

變量類型

接受用戶輸入數據
類型轉換
第二個Python程序_貓咪年齡換算器
String字符串
如何處理String字符串
輸出格式

Print_format

Indexing和Slicing

List列表

List的count與index_method
List中的List
Zip function

Tuple元組

List與Tuple的區別

Dictionary字典

get和setdefault method
字典中的字典

set集合

Boolean運算符號

If語句

邏輯運算符號
比較運算符號
第三個Python App-體重換算

For 循環

Enumerate function
嵌套For Loops

While 循環

第四個Python App-猜數字遊戲
第五個Python App-開車遊戲

循環簡化

循環簡化處理字典

Function

Function參數
Function Return
第六個Python App-自動表情轉換
Except運用
Class
構造函數
繼承

多參數與關鍵字參數

Lambda表達式

如何使用Lambda來排序

全局變量&局部變量

Numpy

Modules And Package

創建自己第一個module
Python內部module
第七個Python App-搖骰子遊戲
如何使用Python內部module查找文件
如何安裝packages
第八個Python App-用openpyxl添加數據及圖表(1)
第八個Python App-用openpyxl添加數據及圖表(2)
為什麼要使用Numpy

Numpy Array的維度與形狀

Numpy Array的一些特性

向量Vector和矩陣Matrix

Random隨機數

Array Indexing & Slicing

Slicing小練習1
Slicing小練習2
Fancy Indexing
Bitwise operators
2D Fancy Indexing
多維度Array

Array的運算

Array運算與Shape
Axis的運用
安裝OpenCV
如何使用Numpy去處理圖片
Matplotlib
安裝Matplotlib
如何使用plot和subplot
Figure和Axes
如何設定圖形的格式
Label與Legend
如何製作柱狀圖
如何製作Histogram
如何製作餅狀圖
如何保存圖表
Pandas
為什麼要使用Pandas
如何使用Pandas讀取Dataframe
在Dataframe如何按條件篩選數據
Dataframe Index
如何使用Pandas讀寫CSV文檔
如何使用Pandas讀寫Excel文檔
如何處理空值-fillna
如何處理空值-interpolate, dropna
如何處理無效值
Pandas處理分組數據
Pandas如何合併數據表
Merge

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Reading list

We haven't picked any books for this reading list yet.
Comprehensive guide to the Python Standard Library, covering its vast collection of modules and their applications.
Comprehensive guide to using Python for financial analysis and modeling, covering data manipulation, financial calculations, and visualization.
Comprehensive guide to deep learning using Python, covering neural networks, convolutional neural networks, and recurrent neural networks.
Practical guide to testing Python code using the pytest framework, covering unit testing, integration testing, and end-to-end testing.
Practical guide to using Python for bioinformatics tasks, covering sequence analysis, genome assembly, and data visualization.
Concise and comprehensive reference to the Python language, covering syntax, built-in functions and objects, and advanced topics.
Practical guide to using Python for basic automation tasks, providing a gentle introduction to Python's core concepts and its practical applications.
Comprehensive guide to the basics of Python programming, covering data types, control flow, functions, object-oriented programming, and debugging.
Provides a comprehensive treatment of machine learning from a probabilistic perspective, covering a wide range of topics from Bayesian inference to deep learning.
While not focused specifically on Machine learning, this book covers a broad range of topics in Artificial Intelligence including machine learning, and good companion to delve deeper into the theoretical and technical aspects of the field.
Practical guide to machine learning for those with no prior experience, covering a wide range of topics from data preprocessing to model evaluation. It great hands-on tutorial to pick up skills in machine learning.
Comprehensive and authoritative reference on deep learning, covering a wide range of topics from neural networks to reinforcement learning.

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