项目介绍

edyoda/data-science-complete-tutorial 是一个托管在 GitHub 上的开源项目,主要使用 Jupyter Notebook 编写,目前获得 1.8k 个 Star、732 次 Fork。

仓库全名
edyoda/data-science-complete-tutorial
Star 数
1.8k
Fork 数
732
未关闭 Issue
5

README 摘要

In person training - https://www.edyoda.com/program/data-scientist-program Machine Learning Git Codebook Lesson 1 : Introduction to Numpy (Video) Lesson 2 : Data Wrangling using Pandas Lesson 3 : Plotting in Python Lesson 4 : Linear Models for Regression & Classification Lesson 5 : Preprocessing Data Lesson 6 : Decision Trees Lesson 7 : Naive Bayes Lesson 8 : Composite Estimators Lesson 9 : Model Selection and…

完整内容见 GitHub 原始 README 或本页 。

常见问题

data-science-complete-tutorial 用什么语言开发?

主要使用 Jupyter Notebook 编写,目前有 1.8k 个 Star。

如何获取 data-science-complete-tutorial 的源码?

可以直接克隆仓库:git clone https://github.com/edyoda/data-science-complete-tutorial.git,默认分支为 master;安装与运行步骤见仓库 README。

data-science-complete-tutorial 最近还在维护吗?

仓库最近一次更新为 2022年10月31日,当前有 5 个未关闭 Issue;Star 数为 1.8k,可作为社区活跃度的参考。

简要分析

热度分
3366
价值分
1328
活跃状态
活跃

语言占比

Jupyter Notebook
Python

README

In person training - https://www.edyoda.com/program/data-scientist-program

Machine Learning Git Codebook

Lesson 1 : Introduction to Numpy (Video)
Lesson 2 : Data Wrangling using Pandas
Lesson 3 : Plotting in Python
Lesson 4 : Linear Models for Regression & Classification
Lesson 5 : Preprocessing Data
Lesson 6 : Decision Trees
Lesson 7 : Naive Bayes
Lesson 8 : Composite Estimators
Lesson 9 : Model Selection and Evaluation
Lesson 10 : Feature Selection Techniques
Lesson 11 : Nearest Neighbors
Lesson 12 : Clustering Techniques
Lesson 13 : Anomaly Detection
Lesson 14 : Support Vector Machines
Lesson 15 : Dealing with Imbalanced Classes
Lesson 16 : Ensemble Methods

Case Study of Classic ML Problems

Case 1 : Linear Regression
Case 2 : Cancer Prediction
Case 3 : Online Learning
Case 4 : Customer Churn Prediction
Case 5 : Income Prediction
Case 6 : Predicting Employee Exit
Case 7 : Face Generation
Case 8 : Finding Similar Houses

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