Explore the foundations of deep learning and learn how artificial neural networks can solve real-world problems.
Description
Welcome to Deep Learning A-Z™—a practical introduction to artificial neural networks, machine learning concepts, and deep-learning workflows.
This course explains the main ideas behind deep learning in a clear and structured way. You will learn how neural networks are built, trained, evaluated, and improved using practical examples.
- Deep learning fundamentals – Understand artificial intelligence, machine learning, deep learning, and the role of neural networks.
- Artificial neural networks – Learn how input layers, hidden layers, output layers, weights, biases, and activation functions work together.
- Model training and evaluation – Explore training data, validation, prediction, performance measurement, and ways to improve a model.
What you will learn
- Understand the difference between AI, machine learning, and deep learning
- Learn the structure of an artificial neural network
- Understand neurons, weights, biases, and activation functions
- Prepare data for a machine-learning project
- Train and test a basic neural-network model
- Understand loss functions and model accuracy
- Recognise overfitting and underfitting
- Improve model performance through practical techniques
- Explore practical uses of deep learning
- Build confidence for further AI and data-science learning
Requirements
- Basic computer skills and an interest in artificial intelligence.
- Basic knowledge of Python is helpful but not essential for understanding the concepts.
- A computer with internet access and a willingness to practise.
Who this course is for
- Beginners interested in artificial intelligence and data science
- Students exploring machine-learning and technology careers
- Developers who want to understand the basics of neural networks
- Professionals curious about how deep-learning models work
