Ready to bring artificial intelligence to life with a real-world project?
Hands-On AI Project: Digit Recognition with Neural Networks is the perfect way to dive into deep learning by building a neural network that can recognize handwritten digitsjust like the ones used in postal systems and banking automation. This project-based course guides you through the entire process of designing, training, and evaluating a neural network using Python and machine learning principles. Youll start from the basics and work your way up to building a model that can accurately classify images from the popular MNIST dataseta foundational milestone in the field of AI.
Through hands-on coding, you'll gain practical experience with data preprocessing, model architecture, activation functions, training algorithms, and performance evaluation. Whether you're new to AI or looking to solidify your knowledge with a tangible project, this course is a fantastic way to turn theory into skill.
What Youll Learn: Introduction to neural networks and image classification How to load and process image data using Python Building and training a neural network from scratch or with libraries like Keras Understanding model accuracy, loss functions, and optimization Visualizing results and improving model performance This course is designed for learners who want a hands-on, practical introduction to AI and machine learning, and who learn best by doing. By the end, youll have built a fully functional digit recognition system and gained valuable insights into how modern AI applications are created.
No prior deep learning experience requiredbasic Python knowledge is all you need.
