This course is part of Andrew Ng’s Deep Learning Specialization and provides a structured introduction to the foundations of deep learning and neural networks. It is designed for beginners who want to understand how modern AI systems learn from data.

The course begins with an overview of deep learning and explains why it has become one of the most powerful technologies in artificial intelligence today. It introduces the concept of neural networks and how they are inspired by the human brain to process information and make predictions.

Learners are guided through supervised learning using neural networks, showing how labeled data is used to train models. The course also explains why deep learning has become so effective in solving complex problems such as image recognition, speech processing, and natural language understanding.

Key topics include binary classification, logistic regression, cost function, and gradient descent. These concepts form the mathematical and practical foundation for training machine learning models.

By the end of this section, learners will understand how neural networks operate, how predictions are made, and how models improve through optimization techniques. This knowledge is essential for progressing into more advanced topics in deep learning such as convolutional networks and sequence models.