Master Regression and Feedforward Networks [2026]

Name: Master Regression and Feedforward Networks [2026]
Rating: 5.0 (86 reviews)

Master Regression analysis and Prediction with Regression Models, Feedforward Neural Networks, and XGBoost Regression

Created byHenrik Johansson

Last updated 1/2026

English

What you'll learn

Master Regression, Regression analysis, 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, XGBoost, 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

Course content

3 sections • 21 lectures • 12h 47m total length

Introduction10:53
This video provides an overview of the course contents, a course description, a presentation of the instructor, and background information about the course
Setup of the Anaconda Cloud Notebook20:47
This video describes the setup procedures for using the Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access and an email address

Note: Anaconda often updates its resources and user interface plus utilizes anti-drone technology. This may cause minor deviations from graphics and procedures in the video
Download and installation of the Anaconda Distribution (optional)20:39
This video describes the procedures to download and install the Anaconda Distribution for use with this course
Download requires internet access
Video is optional

Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
The Conda Package Management System (optional)46:52
This video describes the Conda Package Management System
Conda requires internet access
Video is optional

Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures

Regression, Prediction, and Supervised Learning. Section Overview (I)10:15
This video provides an overview of this section with a table of contents. The concepts of Regression, Prediction, and Supervised Learning are described.
The Traditional Simple Regression Model (II)35:08
Learn to use the traditional simple regression model, some fundamental theory and to create a regression model in a theoretically correct environment with the Scikit-learn and Statsmodels libraries.
The Traditional Simple Regression Model (III)38:00
Learn to use the traditional simple regression model, more fundamental theory, and tools to check and inspect model-fit-to-data, and model assumptions. Learn to create powerful residual plots with Pandas and Matplotlib, and learn to use the R-squared and Durbin-Watson statistics from the Statsmodels summary output.
Some practical and useful modelling concepts (IV)13:01
Learn some practical and useful modeling concepts. Learn about Overfitting, Underfitting, and the Bias-Variance tradeoff.
Some practical and useful modelling concepts (V)13:01
Learn some practical and useful modeling concepts. Learn to use Generalizations with Interpolation and extrapolation. Learn about model interpretation and learn about the fake sample or non-causality concept and about simple or advanced models.
Linear Multiple Regression model (VI)57:00
Create a Linear Multiple Regression Model using correlation matrixes and heatmaps. Learn model Diagnostics and Residual Analysis using both standard package Residual plots and more advanced designed Residual plots.
Linear Multiple Regression model (VII)36:24
Deepen your knowledge about Linear Multiple Regression Models. Introduction to Machine Learning Automatic Model Creation with Forward Selection and Probability-Values.
Multivariate Polynomial Multiple Regression models (VIII)10:13
Learn theory about Multivariate Polynomial Regression Models and Regression terminology. Learn some theory about Automatic model creation (AI) using Machine Learning backward elimination and Regression Models.
Multivariate Polynomial Multiple Regression models (VIIII)1:06:05
Learn to code Multivariate Polynomial Multiple Regression Models combined with the Backward Elimination Feature Selection Algorithm for Machine Learning Automatic Model Creation. Learn to make Feature transformations, Residual Analysis, and some about how to plot advanced high-dimensional model predictions in low dimensional spaces, in a simplified fashion.
Regression Regularization, Lasso and Ridge models (X)1:29:52
Learn about Regularization and to Regularize regression models using Lasso and Ridge Regression. Example regularizing an overfit Polynomial Multiple Regression Model.
Decision Tree Regression models (XI)1:15:26
Learn Decision Tree Regression theory and to implement and regularize Decision Tree Regression models with Scikit-learn. Learn to prepare a dataset for use with Decision Tree Regression models and how to plot Decision Tree graphs and the output of Decision Tree Regression models.
Random Forest Regression (XII)1:09:18
Learn to use Random Forest Regression / Ensembles for Prediction and Regularization. Learn to use importances for model creation and feature selection. Learn how importances change over different subsets of a dataset
Voting Regression (XIII)48:00
Learn to use the Voting Ensemble Regression model for prediction. Learn to use Voting Regression to augment and modify standard Regression models for extended functionality and advanced prediction

Overview2:54
Overview of the section Advanced Machine Learning Models
Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron19:10
Feedforward Multi-Layer Perceptron for Prediction28:50
In this video, non-linear Feedforward Multi-Layer Perceptrons are used on the Medical Costs dataset to predict values with some practical adjustments for enhanced extraordinary Prediction performance
eXtreme Gradient Boosting Regression (XGBoost)55:49
Learn to use eXtreme Gradient Boosting Regression (XGBoost)

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, Regression analysis, 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 and eXtreme Gradient Boosting Regression for prediction tasks. 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, Regression analysis, and Prediction both in theory and practice
Master Regression models from simple linear Regression models to Polynomial Multiple Regression models and advanced Multivariate Polynomial Multiple Regression models plus XGBoost Regression
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, XGBoost, 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 – 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 to

learn to master Regression and Prediction
learn about Automatic Model Creation
learn advanced Data Science and Machine Learning plus improve their capabilities and productivity

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. 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

Master Regression and Feedforward Networks [2026]

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 1hr 39min

Master Regression and Prediction13 lectures • 9hr 22min

Advanced Machine Learning Models4 lectures • 1hr 47min

Requirements

Description

Who this course is for: