【文件属性】：
文件名称：Fast-Track-to-Data-Science-30-Days
文件大小：28.66MB
文件格式：ZIP
更新时间：2021-04-01 08:26:34
JupyterNotebook
DSC 30天研讨会
前往我们的频道观看每日讲座。
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课程大纲
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Python数据科学手册
安装
从了解有关python安装的。
图书
1：100页由安德里·伯科夫（Andriy Burkov）撰写的书籍
2：AurélienGéron-使用Scikit-Learn，Keras和TensorFlow进行动手机器学习_构建智能系统的概念，工具和技术-O'Reilly媒体（2019）
3：AndreasC.Müller，Sarah Guido-Python机器学习简介_数据科学家指南-O'Reilly Media（2016）
4：Foster的商业数据科学
5：动手深度学习算法Sudharsan Ravichandiran（packt）
6：Ian GoodFellow的深度学习
7：Joel Grus撰写的Pyth
【文件预览】：
Fast-Track-to-Data-Science-30-Days-main
----$ day_20 Unsupervised learning in Python()
--------readme.md(381B)
----zz installation()
--------images()
--------readme.md(9KB)
----$ day 4 Inferential Statistics()
--------DSC_Day-04.ipynb(14KB)
--------README.md(78B)
----$ day_25 Cluster Analysis()
--------Species Segmentation with Cluster Analysis Part 2 - Exercise.ipynb(11KB)
--------readme.md(980B)
--------Species Segmentation with Cluster Analysis Part 2 - Solution.ipynb(15KB)
--------Clustering Categorical Data - Solution.ipynb(102KB)
--------Selecting the number of clusters_with_comments.ipynb(44KB)
--------Country clusters.ipynb(28KB)
--------3.01. Country clusters.csv(200B)
--------Categorical.csv(10KB)
--------iris-with-answers.csv(4KB)
--------iris_dataset.csv(2KB)
--------Species Segmentation with Cluster Analysis Part 1- Solution.ipynb(141KB)
--------A Simple Example of Clustering - Solution.ipynb(5KB)
----$ day 9 Data Visualization with Matplotlib()
--------04_01_Simple_Line_Plots.ipynb(294KB)
--------readme.md(794B)
--------04_02_Simple_Scatter_Plots.ipynb(196KB)
--------04_00_Introduction_To_Matplotlib.ipynb(102KB)
----30-days-Data-Science-Workshop.jpg(271KB)
----$ day_14 Exploratory Data Analysis in Python()
--------Data_Preparation_101.ipynb(231KB)
--------readme.md(300B)
----$ day_16 Statistical Analysis()
--------readme.md(352B)
--------Chapter 2 - Data and sampling distributions.ipynb(96KB)
----readme.md(2KB)
----$ day_10 Data-Visualization-with-Seaborn - Basic()
--------readme.md(486B)
--------04_14_Visualization_With_Seaborn.ipynb(1.04MB)
----$ day 3 Descriptive Statistics()
--------README.md(78B)
--------DSC_Day-03.ipynb(5KB)
----$ day_24 Cluster Analysis()
--------readme.md(568B)
----$ day_19 Predicting Credit card approval()
--------Reg_Class_Preprocessing.one(5.01MB)
--------readme.md(1KB)
----$ day_18 Supervised Learning()
--------readme.md(753B)
----$ day_17 Supervised learning()
--------readme.md(140B)
----$ day 7 Data Manipulation with Pandas - I()
--------03_00_Introduction_to_Pandas.ipynb(8KB)
--------readme.md(647B)
--------03_01_Introducing_Pandas_Objects.ipynb(51KB)
----$ day_12 Advance Python - I()
--------03_04_Missing_Values.ipynb(46KB)
--------readme.md(505B)
----$ day_26 Model Validation()
--------readme.md(953B)
--------Course_notes_solutions_answers_Model_Validation_in_Python.pdf(2.12MB)
----zz python_DS_Handbook()
--------readme.md(171B)
----$ day_21 Unsupervised learning in Python()
--------readme.md(435B)
----$ day 6 Intermediate Python()
--------DSC Day 06.ipynb(10KB)
--------README.md(78B)
----$ day_13 Advance Python - II()
--------Data_Preparation_101.ipynb(231KB)
--------readme.md(302B)
----$ day_23 ML WITH TREE BASED MODELS()
--------readme.md(371B)
----openintro-statistics.pdf(20.02MB)
----$ day_11 Data-Visualization-with-Seaborn - Advanced()
--------readme.md(547B)
--------Data_Visualization_in_Python.ipynb(395KB)
----$ day_22 ML WITH TREE BASED MODELS()
--------readme.md(291B)
----$ day 8 Data Manipulation with Pandas - II()
--------readme.md(492B)
--------03_02_Data_Indexing_and_Selection.ipynb(52KB)
----$ day 2 Probability()
--------readme.md(228B)
--------DSC_Day02.ipynb(7KB)
----$ day 1 An Introduction to Data Science()
--------readme.md(227B)
----$ day 5 Introduction to Python()
--------DSC-Day05.ipynb(15KB)
--------README.md(78B)
----$ day_15 Exploratory Data Analysis in Python()
--------Data_Preparation_101.ipynb(231KB)
--------readme.md(352B)
----books.jpg(175KB)
----Resources.md(2KB)
----$ day_27 Model Validation()
--------readme.md(953B)
--------Course_notes_solutions_answers_Model_Validation_in_Python.pdf(2.12MB)