Free online courseNeural Networks and TensonFlow

Course Description

Want to move from hearing about deep learning to actually understanding how it works? This free online course in Artificial Intelligence and Machine Learning guides you through the core ideas behind neural networks and shows how they translate into practical model training with TensorFlow. It is designed for learners who want clarity on the fundamentals and a confident starting point for building real models.

You will learn what a neural network is, why it can recognize patterns from data, and what changes when the network becomes fully connected. Along the way, you will develop an intuitive grasp of what the output layer represents and how its output relates to the type of problem you are solving, such as classification or prediction. Instead of treating concepts like activation functions as jargon, you will understand why they are essential for learning complex relationships and how they affect what a model can express.

The course also focuses on the training process: what it means for a model to learn, how a loss function guides improvement, and why evaluation must be handled carefully. You will see why splitting a dataset into training and testing sets is a must, how it helps you estimate real-world performance, and how it protects you from misleading results. This foundation helps you avoid common pitfalls and communicate your work more professionally.

By the end, you will be able to explain key neural network components in simple terms, follow a basic workflow in TensorFlow with confidence, and make informed decisions when experimenting with data and model settings. Whether you are exploring a new career path or strengthening your programming toolkit, this course helps you take a solid first step into modern AI development.