This comprehensive hands-on course unlocks the power of deep learning for developers, data scientists, and analysts ready to step into the neural network era. Designed for intermediate learners with basic Python and machine learning familiarity, the course starts from core theory and takes you all the way to building custom neural networks from scratch.

You’ll demystify architectures like CNNs, RNNs, and LSTMs while learning how to train and optimize them using real datasets in TensorFlow and PyTorch. The course doesn’t just explain — it gets your hands dirty with practical lab work: classifying images, forecasting time series, performing NLP sentiment analysis, and more. Along the way, you’ll learn how to monitor performance, fight overfitting, and deploy models to real applications.

With step-by-step guidance, code reviews, model debugging tips, and peer feedback, you’ll finish this course with your own portfolio-ready neural network projects — and a deeper understanding of how machines truly learn.

Delivery
Courses are delivered 100% online. Learn on your schedule — videos, case studies, and templates are available instantly upon enrollment. All content is optimized for mobile and desktop.

Refunds
We offer a full refund within 30 days if the course fails to improve your applied deep learning skills.

Language
English

Curriculum

Module 1: Introduction to Neural Networks – Perceptrons, forward/backward propagation, loss functions.
Module 2: Deep Architectures – CNNs for image tasks, RNNs for sequences, LSTMs for memory handling.
Module 3: Training Models in TensorFlow & PyTorch – Layers, optimizers, batch tuning, GPU training.
Module 4: Projects and Deployment – Real-world tasks, model serving, cloud deployment tools.
Capstone Project: Build, train, and deploy your own deep learning application using real-world datasets.