Udemy

Master Regression and Feedforward Networks [2026]

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  • 817 Students
  • Updated 1/2026
5.0
(84 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
12 Hour(s) 47 Minute(s)
Language
English
Taught by
Henrik Johansson
Rating
5.0
(84 Ratings)

Course Overview

Master Regression and Feedforward Networks [2026]

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

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!

Course Content

  • 3 section(s)
  • 21 lecture(s)
  • Section 1 Introduction
  • Section 2 Master Regression and Prediction
  • Section 3 Advanced Machine Learning Models

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


Reviews

  • L
    Linda Jasmin Welter
    5.0

    Excellent regression course! Mint sound and good captions.

  • G
    Gary J MacKenzie
    5.0

    An excellent course. It covers all rhe important details about regression and models. The course is simple to follow and the teacher has a voice which is easy to listen to.

  • C
    Charles Yamamura
    5.0

    This is an excellent course, with clear explanations and a highly constructive approach.

  • R
    Raul V. Rodriguez
    5.0

    Excellent course, excellent materials!

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