This course is designed to take you on an in-depth journey through the world of deep learning and artificial intelligence. Beginning with an introduction to AI and machine learning concepts, you’ll build a solid foundation in neural networks and deep learning with the Keras framework. As you gain confidence, you will explore how neural networks process data, predict outcomes, and solve complex problems.
In the second part of the course, the focus shifts to the powerful Generative Adversarial Networks (GANs). You'll learn how GANs can generate realistic data by pitting two neural networks, the generator and discriminator, against each other. Step-by-step, you will build GAN models using the MNIST dataset, understand their inner workings, and fine-tune them for optimal performance.
By the course's conclusion, you'll be adept at handling various AI and deep learning libraries, training models using large datasets, and deploying deep learning solutions. Whether you're working on image generation or data augmentation, this course will provide you with the expertise needed to excel in today’s AI-driven world.
This course is ideal for intermediate learners with basic Python programming skills and some familiarity with AI or machine learning concepts. You should be comfortable with Python basics, including data structures like lists and dictionaries, and have some experience with data libraries such as NumPy.
Applied Learning Project
The included projects focus on practical applications such as predicting house prices, classifying heart disease, and assessing wine quality, enabling learners to apply deep learning and GAN techniques to real-world problems. These projects provide hands-on experience in data analysis, model building, and deployment, ensuring learners can solve authentic challenges in various domains.
