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Deep Learning with ANN in Python: Build & Optimize

EDUCBA via Coursera

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Overview

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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.

Syllabus

Foundations of Artificial Neural Networks

This module introduces learners to the fundamentals of Artificial Neural Networks (ANN) with Python. It guides them through environment setup, library installation, data preprocessing, and encoding techniques. By the end, learners will understand how to prepare raw data for neural network training using industry-standard practices.

Building and Optimizing ANN Models

This module focuses on constructing, compiling, and optimizing ANN models. Learners will build neural network architectures, apply activation functions, generate predictions, and address data imbalance with resampling methods. The module ensures mastery in both practical implementation and model performance optimization.