Deep Learning is one of the most important fields in modern Artificial Intelligence. It uses artificial neural networks to solve complex problems involving computer vision, natural language processing, time series analysis, recommendation systems, fraud detection, content generation, and many other applications that are part of the daily operations of companies and organizations around the world.

Deep Learning techniques power a wide range of modern solutions, including intelligent assistants, generative AI models for text and images, AI-assisted medical diagnosis, autonomous vehicles, advanced recommendation systems, image and speech recognition, demand forecasting, financial analysis, and drug discovery. Although Artificial Intelligence has evolved rapidly in recent years with the emergence of foundation models, neural networks remain the core technology behind these breakthroughs.

The demand for professionals who can develop, train, evaluate, and deploy Deep Learning models continues to grow across technology companies, fintechs, industries, startups, research centers, and organizations in virtually every sector. Today, knowledge of Artificial Intelligence and Machine Learning is considered a valuable skill for software developers, data analysts, data scientists, and technology professionals.

To help you enter this exciting field, this course provides a comprehensive learning experience that combines theoretical foundations with practical applications using Python and the leading tools in the Machine Learning ecosystem. The content is carefully structured to guide you from the fundamentals to more advanced Deep Learning techniques, giving you the knowledge needed to understand, build, and adapt neural network models for real-world problems.

The course is organized into seven major modules:

• Artificial Neural Networks (ANNs)

• Convolutional Neural Networks (CNNs)

• Recurrent Neural Networks (RNNs)

• Self-Organizing Maps (SOMs)

• Boltzmann Machines

• Autoencoders

• Generative Adversarial Networks (GANs)

In each module, you will learn the underlying concepts, understand how the algorithms work, and implement practical projects step by step using Python.

Some of the projects developed throughout the course include:

• Classifying tumors as benign or malignant

• Plant species classification

• Predicting used vehicle prices

• Forecasting video game sales

• Handwritten digit recognition

• Cat and dog image classification

• Homer and Bart image classification from The Simpsons

• Object recognition in images (airplanes, automobiles, birds, cats, horses, trucks, and more)

• Building time series models to predict stock prices

• Predicting air pollution levels

• Clustering wines based on their characteristics

• Grouping medical records for exploratory analysis

• Detecting potential fraud in financial datasets

• Dimensionality reduction for images and complex datasets

• Building movie recommendation systems

• Comparing neural-network-based recommendation systems with traditional collaborative filtering approaches

• Generating new images using generative neural networks

At the end of each theoretical module, you will find quizzes to reinforce the concepts covered, along with additional resources for further study. The practical sections include programming exercises and complete projects with fully worked solutions, allowing you to compare your implementation and strengthen your understanding.

This course is designed for students, professionals, and technology enthusiasts at different experience levels. If this is your first exposure to Deep Learning, Machine Learning, or Neural Networks, you will have access to an introductory appendix covering the essential concepts needed to get started.

The only mandatory prerequisite is a basic understanding of programming logic. Advanced Python knowledge is not required, as all examples are explained in detail throughout the course.

If you want to build a strong foundation in Deep Learning and learn how to develop practical neural network applications, this course is for you.

See you in class!