Simplified: All About Neural Networks
What you'll learn
- Neural Networks
- Machine Learning
- Applications of Neural Networks
- Forward Propagation
- Backward Propagation
- Types of Neural Networks
- Basic Understanding of Programming Languages & Computer Science
In this sequel series, we will learn the basics of what a neural network is, how they're used in the real world, and two in-depth projects that allow us to use our skills in an applicable program. This course is dedicated to teaching students with an understanding of basic computer science concepts and little to no pre-existing knowledge of machine learning. Specifically, "Machine Learning Simplified" targets individuals who can't afford an expensive machine learning course and do not have the extensive pre-requisites the majority of courses require. In fact, the only major pre-requisite for taking this course is taking the first course in this series which is also available on Udemy for free. This course is divided into four major sections. The first one covers a brief introduction into neural networks and how they're used in the real world. The second section goes in depth about the structure and mechanisms of neural networks. In the third section, we program a fully functional neural network using Spyder from Anaconda Navigator. Lastly, we conclude the course by discussing two types of special neural networks. Machine learning is a critical concept that is becoming very relevant in the status quo. So, before it's too late, join Simplified: All About Neural Networks and learn this topic as simply as possible!
Who this course is for:
- High school students with limited computer science knowledge
- Individuals interested in machine learning but don't have the extensive pre-requisites other courses require
- Students who can't afford the expensive machine learning certification courses
I am a rising senior enrolled at the Academies of Loudoun with an interest in machine learning and app development. I have studied machine learning at my school and completed a machine learning course at Stanford University. Additionally, I have successfully applied my machine learning skills in the real world in various projects.