Master Regression and Feedforward Networks [2024] - Learn Everything About Regression Models

This is a course called "Master Regression and Feedforward Networks [2024]" that will teach you everything about Regression and Prediction. We will examine a variety of regression models, from basic ones to more sophisticated techniques, including Polynomial Multiple Regression and Multivariate Polynomial Multiple Regression, learning both theory and practice.

In this course, we have tools for automatically creating models and selecting the best features for your predictions. You will improve your own models with methods like Lasso and Ridge Regression to include greater accuracy. We will also touch on Decision Trees, Random Forests, and Voting regressions, which offer many possibilities of usage. Oh yes, we can never forget mastering Feed-forward Multilayer Networks or any other advanced regression structures.

In this class, “Master Regression and Feedforward Networks [2024],” we shall use Python libraries like Statsmodels and Scikit-learn coupled with visualisation tools such as Matplotlib, Seaborn, and Pandas to effectively implement various regression solutions. Additionally, we’ll cover Cloud computing using platforms like Anaconda Cloud Notebook, where you can work with cloud resources for data analysis like an expert.

Master Regression and Feedforward Networks [2024] Table of Contents:

1. Introduction - 10:53
2. Setup of the Anaconda Cloud Notebook - 14:03
3. Download and installation of the Anaconda Distribution (optional) - 21:05
4. The Conda Package Management System (optional) - 35:00
5. Regression, Prediction, and Supervised Learning. Section Overview (I) - 10:15
6. The Traditional Simple Regression Model (II) - 35:08
7. The Traditional Simple Regression Model (III) - 38:00
8. Some practical and useful modeling concepts (IV) - 13:01
9. Some practical and useful modeling concepts (V) - 13:01
10. Linear Multiple Regression model (VI) - 57:00
11. Linear Multiple Regression model (VII) - 36:24
12. Multivariate Polynomial Multiple Regression models (VIII) - 10:13
13. Multivariate Polynomial Multiple Regression models (VIIII) - 01:06:05
14. Regression Regularization, Lasso and Ridge models (X) - 01:29:52
15. Decision Tree Regression models (XI) - 01:15:26
16. Random Forest Regression (XII) - 01:09:18
17. Voting Regression - 32:00
18. Overview - 02:45
19. Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron - 19:10
20. Feedforward Multi-Layer Perceptron for Prediction - 28:50

Who is this course for?

1. It’s meant for anyone who wants to be good at Regression and Prediction
2. Students who want to learn about Automatic Model Creation
3. Students who want to learn advanced Data Science and Machine Learning 

Click on the links below to Download Master Regression and Feedforward Networks [2024]!