掌握Python数据分析和建模基础知识视频教程

一个使用Jupyter notebook、Numpy、SciPy、Pandas、Matplotlib、Statmodels、Scikit-learn等等的真实项目

你会学到什么
数据分析和建模过程
设置Python数据分析和建模环境
数据探索
重命名数据列
数据切片、排序、过滤和分组数据
缺失值检测和插补
异常检测和处理
相关性分析和特征选择
用于模型拟合和测试的分割数据集
不同方法的数据规范化
开发经典的统计线性回归模型
开发机器线性回归模型
解释模型结果
改进模型
评估模型
可视化模型结果

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
语言：英语+中英文字幕（云桥CG资源站 机译） |时长:34节课(7小时2分钟)|大小解压后:3.5 GB


要求
理解代码所需的基本Python语言知识

描述
我们生活在一个数据爆炸的世界，数据无处不在，因此建立数据分析和建模技能至关重要。根据TIOBE指数，自2021年10月以来，Python已经超过Java和C，成为当今最受欢迎的编程语言。根据KDnuggets民意调查，Python领先于顶级数据科学和机器学习平台。Master Python Data Analysis and Modelling Essentials

本课程使用真实世界的项目和数据集以及众所周知的Python库，向您展示如何探索数据、发现问题并修复它们，以及如何以一种易于理解的方式逐步开发经典的统计回归模型和机器学习回归。学完本课程后，您将掌握以下技能

(1)使用Python熊猫库来探索数据

(2)使用不同的方法重命名数据列

(3)通过不同的方法检测数据集中的缺失值和异常值

(4)用不同的方法填补缺失和处理异常值

(5)进行相关性分析，并在分析的基础上选择特征

(6)用不同的方法对分类变量进行编码

(7)分割数据集用于模型训练和测试

(8)用标度方法归一化数据

(9)开发经典统计回归模型和机器学习回归模型

(10)拟合模型、改进模型、评估模型和可视化建模结果，等等

这门课程是给谁的
商业分析师
数据分析专业人员
统计学家
数据分析、建模和机器学习的工程师和科学家
任何希望为自己的项目学习使用Python进行数据分析和建模的人


MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 34 lectures (7h 2m) | Size: 2.97 GB

A Real-World Project using Jupyter notebook, Numpy, SciPy, Pandas, Matplotlib, Statmodels, Scikit-learn, and many more

What you’ll learn
Data analysis and modelling process
Setting up Python data analysis and modelling environment
Data exploration
Rename the data columns
Data slicing, sorting, filtering, and grouping data
Missing value detection and imputation
Outlier detection and treatment
Correlation Analysis and feature selection
Splitting data set for model fitting and testing
Data normalization with different methods
Developing a classic statistical linear regression model
Developing a machine linear regression model
interpreting the model results
Improving the models
Evaluating the models
Visualizing the model results

Requirements
Basic Python language knowledge needed to understand the codes

Description
We are living in data explosive world where data is ubiquitous, and thus it is essential to build data analysis and modelling skills. Based on TIOBE Index, Python has overpassed Java and C and become the most popular programming language of today since October 2021. Python leads the top Data Science and Machine Learning platforms based on KDnuggets poll.

This course use a real world project and dataset and well known Python libraries to show you how to explore data, find the problems and fix them, and how to develop classic statistical regression models and machine learning regression step by step in an easily undrstand way. After this course, you will own the skills to

(1) to explore data using Python Pandas library

(2) to rename the data column using different methods

(3) to detect the missing values and outliers in dataset through different methods

(4) to use different methods to fill in the missings and treat the outliers

(5) to make correlation analysis and select the features based on the analysis

(6) to encode the categorical variables with different methods

(7) to split dataset for model training and testing

(8) to normalize data with scaling methods

(9) to develop classic statistical regression models and machine learning regression models

(10) to fit the model, improve the model, evaluate the model and visulize the modelling results, and many more

Who this course is for
Business analysts
Data analytics professionals
Statisticians
Engineers and scientists for data analysis, modelling and machine learning
Anyone who wants to learn data analysis and modelling with Python for his/her projects
云桥CG资源站 为三维动画制作，游戏开发员、影视特效师等CG艺术家提供视频教程素材资源！