United States Courses & Certifications

Stanford CS336 - Language Modeling from Scratch 2025

Master language model development from tokenization to deployment, covering transformers, GPU optimization, scaling laws, data processing, and alignment techniques in this comprehensive Stanford program.

• YouTube
• 22 hours 19 minutes
• Self-Paced
• Free Video

Validation of Safety Critical Systems

Master mathematical validation techniques for autonomous systems using sampling methods, formal verification, and reachability analysis in safety-critical applications.

• YouTube
• 18 hours 50 minutes
• Self-Paced
• Free Video

Stanford CS234 - Reinforcement Learning - Spring 2024

Master reinforcement learning fundamentals through Stanford's comprehensive program covering MDP planning, policy evaluation, Q-learning, exploration strategies, and deep RL applications.

• YouTube
• 20 hours 11 minutes
• Self-Paced
• Free Video

Stanford CS236 - Deep Generative Models I 2023

Master probabilistic foundations and algorithms for deep generative models including VAEs, GANs, normalizing flows, and diffusion models with applications across AI domains.

• YouTube
• 23 hours 38 minutes
• Self-Paced
• Free Video

Data Compression - Theory and Applications

Master data compression theory and applications, from lossless techniques like Huffman coding to lossy methods including JPEG and modern ML approaches for efficient information representation.

• YouTube
• 23 hours 42 minutes
• Self-Paced
• Free Video

Statistical Learning with Python

Master statistical learning fundamentals through hands-on Python implementation, covering regression, classification, neural networks, and unsupervised methods without heavy mathematics.

• YouTube
• 20 hours 18 minutes
• Self-Paced
• Free Video

Convex Optimization I - 2023

Master convex optimization fundamentals and applications across engineering, finance, and machine learning through Stanford's comprehensive mathematical framework and solution techniques.

• YouTube
• 23 hours 44 minutes
• Self-Paced
• Free Video

Machine Learning with Graphs

Master graph-based machine learning techniques including Graph Neural Networks, PageRank, knowledge graphs, and network analysis for modeling complex social, technological, and biological systems.

• YouTube
• 1 day 8 minutes
• Self-Paced
• Free Video

Stanford CS109 - Introduction to Probability for Computer Scientists 2022

Master probability theory fundamentals through combinatorics, random variables, distributions, and machine learning applications in this comprehensive Stanford computer science program.

• YouTube
• 1 day 11 hours 25 minutes
• Self-Paced
• Free Video

Stanford CS224N - Natural Language Processing with Deep Learning 2023

Master cutting-edge neural networks for NLP, from word vectors to Transformers, with hands-on tutorials covering RNNs, attention mechanisms, and modern language models.

• YouTube
• 1 day 5 hours 12 minutes
• Self-Paced
• Free Video

Natural Language Understanding I - Spring 2023

Master advanced NLP techniques including transformers, BERT, GPT, in-context learning, and behavioral evaluation methods for natural language understanding systems.

• YouTube
• 15 hours 29 minutes
• Self-Paced
• Free Video

Principles of Sensing for Autonomy I - Spring 2023

Explore fundamental physics principles behind GPS, inertial sensors, radar, lidar, and cameras for autonomous robotics applications and sensor design.

• YouTube
• 1 day 4 hours 44 minutes
• Self-Paced
• Free Video

Stanford CS229 - Machine Learning I Spring 2022

Master supervised and unsupervised learning, neural networks, reinforcement learning, and statistical pattern recognition through Stanford's comprehensive machine learning curriculum.

• YouTube
• 1 day 1 hour 43 minutes
• Self-Paced
• Free Video

Deep Multi-Task and Meta Learning - Autumn 2022

Master multi-task and meta-learning algorithms to build AI systems that efficiently learn new tasks from limited data and transfer knowledge across domains.

• YouTube
• 21 hours 30 minutes
• Self-Paced
• Free Video

Machine Learning Explainability Workshop

Dive into modern machine learning explainability techniques with Harvard's Prof. Hima Lakkaraju, covering interpretable models, post-hoc methods, and evaluation frameworks.

• YouTube
• 4 hours 12 minutes
• Self-Paced
• Free Video