Master Regression and Feedforward Networks [2024]

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Henrik Johansson

9:58:50

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1. Introduction

1. Introduction.mp4

10:53

2. Setup of the Anaconda Cloud Notebook.mp4

14:03

3. Download and installation of the Anaconda Distribution (optional).mp4

21:05

4. The Conda Package Management System (optional).mp4

35:00

2. Master Regression and Prediction

1. Regression, Prediction, and Supervised Learning. Section Overview (I).mp4

10:15

2. The Traditional Simple Regression Model (II).mp4

35:08

3. The Traditional Simple Regression Model (III).mp4

38:00

4. Some practical and useful modelling concepts (IV).mp4

13:01

5. Some practical and useful modelling concepts (V).mp4

13:01

6. Linear Multiple Regression model (VI).mp4

57:00

7. Linear Multiple Regression model (VII).mp4

36:24

8. Multivariate Polynomial Multiple Regression models (VIII).mp4

10:13

9. Multivariate Polynomial Multiple Regression models (VIIII).mp4

01:06:05

10. Regression Regularization, Lasso and Ridge models (X).mp4

01:29:52

11. Decision Tree Regression models.mp4

24:57

12. Random Forest Regression.mp4

41:08

13. Voting Regression.mp4

32:00

Files.zip

3. Advanced Machine Learning Models

1. Overview.mp4

02:45

2. Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron.mp4

19:10

3. Feedforward Multi-Layer Perceptron for Prediction.mp4

28:50

Description

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

Who is this 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

What You Need to Know?

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

More details

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 practice
Master Regression models from simple linear 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 modelsâ€™ 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 â€“ golden nuggets to improve your quality of work life.
And much moreâ€¦

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

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Henrik has a wide instructor/lecturer experience with more than 20 years in roles ranging from University teacher to sports coach to leadership roles in the private and public sectors.Henrik has experience teaching students from all walks of life, from the poor to royalty, and has taught students from nearly all educational backgrounds, from high school to Ph.D.s.Courses given by Henrik are intended to have unique content, and will teach you many new things.

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