TensorFlow is one of the most widely used frameworks for developing Deep Learning and Artificial Intelligence solutions with Python. In this course, you'll learn how to build neural networks from scratch, train them with real data, and deploy them to production using the most important tools in the TensorFlow ecosystem.

Throughout the course you'll build practical projects with artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), Transfer Learning and Reinforcement Learning (Deep Q-Learning). You'll understand not only how these models work, but also when to use them and how to apply them to solve real problems in classification, prediction and decision-making.

Beyond model training, you'll learn the complete workflow of a professional Machine Learning project. You'll discover how to validate and transform data with TensorFlow Extended (TFX), create APIs with Flask, serve models through TensorFlow Serving, optimize neural networks for mobile devices with TensorFlow Lite, and accelerate training using multiple GPUs.

This course is designed for Python developers, data scientists, Machine Learning engineers, and anyone who wants to master TensorFlow from its foundations to production environments. It combines theory, practical examples, and complete projects so you gain applicable experience from day one.

If you want to learn how to develop Artificial Intelligence solutions from start to finish, understand how neural networks work under the hood, and take your models from a notebook to real-world applications capable of handling thousands or millions of requests, this course will give you the skills you need to make it happen.