Build practical deep learning skills and learn to build, train, evaluate, and optimize neural networks with PyTorch. You’ll begin by distinguishing machine learning from deep learning and exploring perceptrons, neural networks, and their role in real-world AI systems. You’ll then set up Jupyter Notebooks, Google Colab, and PyTorch before working with tensors, gradients, hidden layers, and transfer learning.

Through guided coding exercises and case studies, you’ll prepare image datasets and develop classification models using MNIST, CIFAR-10, and CIFAR datasets. You’ll also apply CNNs to text classification, compare loss functions, and generate context-aware text with transformers. Advanced lessons cover language models, sequence generation, encoder-decoder architectures, attention-based translation, training pipelines, and performance evaluation. You’ll extend these techniques to tabular prediction through preprocessing and feature engineering, then compare collaborative and content-based filtering for recommender systems. Designed for learners seeking practical AI, deep learning, or data science skills, this course uniquely progresses from beginner-friendly concepts to advanced vision, NLP, attention, and recommendation projects. Enroll to turn deep learning theory into working models across image, text, structured data, and recommendation tasks.