Deep Learning with ANN in Python: Build & Optimize

Overview

By the end of this course, learners will be able to configure a Python environment, preprocess and encode data, build Artificial Neural Network (ANN) architectures, generate predictions, and address imbalanced datasets using resampling techniques. Participants will gain hands-on experience with TensorFlow, Keras, and Anaconda while mastering practical skills in data preparation, model construction, and performance optimization. This course benefits students, data enthusiasts, and professionals seeking to strengthen their deep learning expertise with a focused, project-based approach. Unlike generic tutorials, it emphasizes a complete end-to-end workflow—from environment setup and data preprocessing to ANN design and evaluation—ensuring learners can independently create predictive models. What makes this course unique is its balance between conceptual clarity and real-world implementation. Learners not only understand the theory but also apply it directly to customer churn analysis, a practical business use case. With step-by-step lessons, quizzes, and guided projects, this course equips participants with the confidence to implement ANN models in real scenarios and transition smoothly into more advanced deep learning topics.

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.