Neural networks and deep learning are the foundation for modern Artificial Intelligence (AI), and key concepts for anyone curious about how tools like ChatGPT, image classifiers, or self-driving cars work.

In this course, we’ll break down what neural networks are by introducing simple building blocks like weights, biases, and activation functions. You’ll learn how neural networks are structured using layers of interconnected nodes, and see how data flows through them to make predictions.

From there, we’ll walk through the full model training process, including forward passes, loss functions, backpropagation, and gradient descent. Each concept is explained visually and intuitively, with just enough math to understand what’s happening under the hood.

Then, we’ll extend these ideas into the world of deep learning, introducing popular architectures like CNNs, RNNs, LSTMs, and Transformers, and showing how they’re used in real-world applications across computer vision and natural language processing (NLP).

If you’re looking for a visual, no-code introduction to neural networks and deep learning, this is the course for you.

NOTE: This course is part of the Natural Language Processing in Python course, which is a more comprehensive overview of all the essential concepts for Natural Language Processing (NLP) in Python.

Who should take this course?

• Analysts, data scientists, or anyone curious about how AI models like ChatGPT actually work

• Beginners who want an intuitive, visual introduction to neural networks and deep learning concepts

• Anyone looking to build a strong conceptual foundation before diving into deep learning code

What are the course requirements?

• No coding experience required

• Basic high school math (like formulas for a line, weighted sums, etc.)

• Some prior machine learning knowledge is helpful, but not necessary — we explain everything step-by-step with clear visuals