用回归模型和前馈神经网络进行回归分析和预测
你将学到什么：
从理论和实践两方面掌握回归预测
掌握回归模型，从简单回归模型到多项式多元回归模型和高级多元多项式多元回归模型
利用机器学习自动建立模型和特征选择
使用回归模型的正则化以及用Lasso和Ridge回归来正则化回归模型
使用决策树、随机森林和投票回归模型
使用前馈多层网络和高级回归模型结构
使用有效的先进残差分析和工具来判断模型的拟合优度和残差分布
使用Statsmodels和Scikit-learn库进行Matplotlib、Seaborn、Pandas和Python支持的回归
云计算：使用Anaconda云笔记本（基于云的Jupyter笔记本）。学会使用云计算资源
选项：使用Anaconda发行版（适用于Windows、Mac、Linux）
选项：使用Python environment fundamentals与Conda包管理系统和命令行一起安装/更新库和包
要求：
推荐使用Windows、MacOS、iOS、Android、ChromeOS或Linux电脑的日常经验
通过internet连接访问计算机
课程只使用免费软件
包括云计算和Windows 10/11的安装和设置视频
一些蟒蛇和熊猫的技能是必要的
说明：
欢迎来到回归和前馈网络大师课程！本课程将教你掌握回归和预测，使用大量高级回归技术进行预测和机器学习自动模型创建，即所谓的真机器智能或AI。你将学习处理预测任务的高级模型结构，您将学习建模理论和几种有用的方法，为回归模型的数据分析准备数据集。您将学习：在理论和实践中掌握回归和预测掌握回归模型从简单的线性回归模型到多项式多元回归模型和高级多元多项式多元回归模型使用机器学习自动模型创建和特征选择使用回归模型的正则化以及使用套索和岭回归正则化使用决策树、随机森林、投票回归模型使用前馈多层网络和高级回归模型结构使用有效的高级残差分析和工具来判断模型的拟合优度和残差分布。使用Statsmodels和Scikit-learn库进行Matplotlib、Seaborn、Pandas和PythonCloud计算支持的回归：使用Anaconda云笔记本（基于云的Jupyter笔记本）。学习使用云计算资源。选项：使用Anaconda发行版（适用于Windows、Mac、Linux）选项：将Python环境基础知识与Conda包管理系统和命令行一起使用，安装/更新库和包——这是提高您工作质量的金块。还有更多…本课程是学习掌握回归和预测的绝佳途径！回归和预测是建模、预测、人工智能、和预测。本课程专为希望学习掌握回归和预测的每个人设计了解自动模型创建学习高级数据科学和机器学习并提高他们的能力和生产力要求：建议每天使用Windows、MacOS、iOS、Android、ChromeOS或Linux的计算机访问具有互联网连接的计算机本课程仅使用免费软件云计算和Windows 10/11的演示安装和设置视频一些Python和Pandas技能是必要的。如果你缺少这些，“用熊猫和Python掌握回归和预测”这门课程包含了你需要的所有知识。如果我们能够穿越时空成为新生，这门课程是我们自己都希望能够参加的。在我们看来，这门课是学习掌握回归和预测的最佳课程。立即注册，即可获得10多个小时的视频教程，配有手动编辑的英文字幕，并在完成课程后获得结业证书！
本课程的对象：
任何想学习回归和预测的人
任何想学习自动模型创建的人
任何想学习高级数据科学和机器学习并提高能力和生产力的人
Master Regression and Feedforward Networks [2024]
Published 5/2024
Created by Henrik Johansson
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 20 Lectures ( 9h 58m ) | Size: 4.3 GB
Master Regression analysis and Prediction with Regression Models and Feedforward Neural Networks
What you'll learn:
Master Regression and Prediction both in theory and practice
Master Regression models from simple Regression models to Polynomial Multiple Regression models and advanced Multivariate Polynomial Multiple Regression models
Use Machine Learning Automatic Model Creation and Feature Selection
Use Regularization of Regression models and to regularize regression models with Lasso and Ridge Regression
Use Decision Tree, Random Forest, and Voting Regression models
Use Feedforward Multilayer Networks and Advanced Regression model Structures
Use effective advanced Residual analysis and tools to judge models’ goodness-of-fit plus residual distributions
Use the Statsmodels and Scikit-learn libraries for Regression supported by Matplotlib, Seaborn, Pandas, and Python
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages
Requirements:
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection
The course only uses costless software
Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included
Some Python and Pandas skills are necessary
Description:
Welcome to the course Master Regression and Feedforward Networks!This course will teach you to master Regression and Prediction with a large number of advanced Regression techniques for purposes of Prediction and Machine Learning Automatic Model Creation, so-called true machine intelligence or AI.You will learn to handle advanced model structures for prediction tasks, and you will learn modeling theory and several useful ways to prepare a dataset for Data Analysis with Regression Models.You will learn to:Master Regression and Prediction both in theory and practiceMaster Regression models from simple linear Regression models to Polynomial Multiple Regression models and advanced Multivariate Polynomial Multiple Regression modelsUse Machine Learning Automatic Model Creation and Feature SelectionUse Regularization of Regression models and to regularize regression models with Lasso and Ridge RegressionUse Decision Tree, Random Forest, and Voting Regression modelsUse Feedforward Multilayer Networks and Advanced Regression model StructuresUse effective advanced Residual analysis and tools to judge models’ goodness-of-fit plus residual distributions.Use the Statsmodels and Scikit-learn libraries for Regression supported by Matplotlib, Seaborn, Pandas, and PythonCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources.Option: To use the Anaconda Distribution (for Windows, Mac, Linux)Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work life.And much more…This course is an excellent way to learn to master Regression and Prediction!Regression and Prediction are the most important and commonly used tools for modeling, prediction, AI, and forecasting.This course is designed for everyone who wants tolearn to master Regression and Predictionlearn about Automatic Model Creationlearn advanced Data Science and Machine Learning plus improve their capabilities and productivityRequirements:Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedAccess to a computer with an internet connectionThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedSome Python and Pandas skills are necessary. If you lack these, the course "Master Regression and Prediction with Pandas and Python" includes all knowledge you need.This course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Regression and Prediction.Enroll now to receive 10+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
Who this course is for:
Anyone who wants to learn to master Regression and Prediction
Anyone who wants to learn about Automatic Model Creation
Anyone who wants to learn advanced Data Science and Machine Learning plus improve their capabilities and productivity
