Master the fundamentals of Deep Learning by building and optimising Artificial Neural Networks (ANNs) in Python through a structured, hands-on learning experience. This course guides you from configuring a Python environment with Anaconda and TensorFlow to preprocessing and encoding data, constructing ANN architectures, generating predictions, and improving model performance with resampling techniques for imbalanced datasets.

Designed for students, data enthusiasts, and professionals looking to strengthen their deep learning skills, the course combines practical implementation with clear explanations to help you understand every stage of the ANN workflow. You will learn how to prepare data for training, build neural network models using TensorFlow and Keras, apply activation functions, evaluate predictions, and optimise model performance using industry-standard practices. A distinguishing feature of this course is its end-to-end, project-based approach. Rather than focusing on isolated concepts, it connects environment setup, data preparation, model development, and evaluation into a complete workflow using a customer churn analysis scenario. Through guided lessons, practical exercises, and quizzes, you will gain the confidence to build, evaluate, and optimise ANN models in Python while developing a strong foundation for further study in deep learning.