Artificial Neural Network for Regression

Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant

4.63 (6120 reviews)

Udemy

platform

English

language

Data Science

What you will learn

How to implement an Artificial Neural Network in Python

How to do Regression

How to use Google Colab

Charts

Students

Price

Rating & Reviews

Enrollment Distribution

Comidoc Review

Our Verdict

This course offers a solid introduction to ANN for regression tasks. However, expect some areas of improvement, particularly in clarifying essential concepts, providing comprehensive explanations regarding various parameter choices, and optimizing model parameters. The course's value lies mostly in offering hands-on experience using a real dataset on Google Colab.\n

What We Liked

Covers the essential aspects of building an Artificial Neural Network (ANN) for regression tasks using Python and Google Colab.
Includes a comprehensive introduction to the Combined Cycle Power Plant dataset, allowing for a clear understanding of the problem being addressed.
Features effective structuring of content, which simplifies following along and implementing the techniques discussed.
The Ligency Team's initiative to create a Discord community for students adds a collaborative learning experience.

Potential Drawbacks

There might be instances of wordiness that could potentially confuse learners without prior ANN knowledge.
Lacks feature scaling explanation, which may impact the predicted values and understanding of regression concepts.
Insufficient explanation regarding parameter values chosen for adding ANN layers.
Inadequate guidance on optimizing various parameters like learning rate, number of hidden layers, and neurons in each layer.