
수학으로부터 인류를 자유롭게 하라(기초대수학편)
공대형아(신경식)
중고등학교 과정에서 배우는 수학 내용들을 압축한 강의입니다. 필요한 수학적 지식, 테크닉들을 각 아이템마다 많은 연습과 함께 배웁니다.
입문
대수학
This is a lecture on data visualization using the Matplotlib library.
Okay, I will translate the following Korean text into English: 안녕하세요. 저는 파이썬을 좋아합니다. Translation: Hello. I like Python.
Matplotlib
Data Visualization
This lecture is part of the pre-semester (preparatory semester before this semester) of the artificial intelligence specialized curriculum All about AI.
Data processing libraries: NumPy, Matplotlib, Pandas
This lecture covers Matplotlib, the core of data visualization .
Miro Link: https://miro.com/app/board/uXjVNJ8PZSs=/?share_link_id=801072444784
For an introduction to All About AI, please refer to the orientation lecture.
In the Python world, Matplotlib is the de facto standard library for data visualization.
Like NumPy, once you learn it properly, it is a library that will be of great help in any field you learn in the future.
In this course, you will learn Matplotlib more completely than any other training material .
This course consists of 4 parts and 18 chapters.
Part.I Matplotlib Preview
Chap1 Visualization Preview with PyPlot Interfaces
Chap2 Visualization Preview with OOP Interfaces
Part.II Matplotlib Anatomy
Chap3 Matplotlib Anatomy Prerequisites
Chap4 Figure Objects
Chap5 Axes Objects
Chap6 Text Objects
Chap7 Spines, Ticks and Grids
Chap8 Axis Objects and Legends
Chap9 Colormaps and Colorbars
Chap10 rcParams and Styles
Chap11 Transformations
Part.III Plotting APIs
Chap12 Pairwise Data Visualizations
Chap13 Statistical Distributions Visualizations
Chap.14 Gridded Data Visualizations
Chap.15 3D Data Visualizations
Part IV Visualization Hands-on Practices
Chap.16 EDA on the Iris Species
Chap.17 MNIST Classification
Chap.18 EDA on the London Bike Sharing
The following parts are introduced in reverse chronological order to explain what you need to learn to achieve your goals.
Ultimately, through this course we will learn the ability to visualize data the way we want.
To this end, in the final Part VI, we will practice processing and visualizing the most commonly used data in machine learning.
Here is an example of a visualization that we will be creating together in Part VI.
To draw graphs like Part.VI, you need to learn how to use various plotting APIs provided by Matplotlib.
Matplotlib provides the following types of plotting:
In this lecture, we will learn how to use all the plotting APIs except the less commonly used graphs in the figures above.
Before we dive right into Part III, in Part II we'll learn how to use elements that are common to all graphs .
As you can see in this image, elements like Figure, Axes, Text, Spine, XAXis, YAxis, Tick, Ticklabel, and Grid apply to all graphs.
So in Part II, before we start drawing graphs in earnest, we'll learn how to handle these common elements as we wish.
This will allow you to learn common elements that apply to the world of Matplotlib all at once, and systematically learn how Matplotlib works.
In Part II, we will learn the components of Matplotlib graphs one by one.
However, if you learn this content right away, it may sound like something out of the blue.
So in the very first part, Part.I , we will get a taste of the overall process of drawing graphs using Matplotlib.
Learn briefly about the two interfaces used in Matplotlib, and why the OOP Interface is more powerful than the PyPlot Interface.
This will help you understand where and how the things you learn next apply.
When using Python's Matplotlib in practice, there is a lot of content, so it is difficult to memorize and use everything.
The lecture notes for this course will serve as reference notes to help you work quickly by looking at the lecture notes instead of having to search for materials on the Internet inefficiently.
Who is this course right for?
Anyone who works with data using Python
For those learning machine learning and deep learning
Need to know before starting?
Numpy Basics
Basic Python Syntax
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103 lectures ∙ (19hr 55min)
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