Computer Vision
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10 lessons3 levels1 capstone project
Apply deep learning to images and video through classification, convolutional networks, transfer learning, detection, segmentation and practical computer-vision pipelines.
BASIC
- Lecture 1: Introduction — Stanford CS231N 2025 (Long)
World-class overview of modern computer vision and deep-learning approaches. - Image Classification with CNNs — Stanford CS231N (Long)
Deepens convolutional architectures and image-classification design. - Introduction to Convolutional Neural Networks for Visual Recognition — Stanford (Long)
Foundational CNN intuition and visual- recognition pipeline.
INTERMEDIATE
- Deep Learning Lecture 1 — MIT OpenCourseWare
Apply Computer Vision through the adjacent capability: Deep Learning. - Introduction to Deep Learning 2026 — MIT 6.S191
Apply Computer Vision through the adjacent capability: Deep Learning. - Introduction to Natural Language Processing
Apply Computer Vision through the adjacent capability: Natural Language Processing (NLP).
ADVANCED
- End-to-End NLP Project in Python
Extend Computer Vision into senior/production practice through Natural Language Processing (NLP). - Step by Step — Simple Neural Network with TensorFlow
Extend Computer Vision into senior/production practice through Deep Learning. - End-to-End Databricks Data Engineering Project
Extend Computer Vision into senior/production practice through Data Engineering.