Supervised Learning Courses and Certifications

Yannic Kilcher

Reinforcement Learning Upside Down - Don't Predict Rewards, Just Map Them to Actions

Innovative approach to reinforcement learning that maps desired rewards to actions, transforming it into supervised learning. Shows promising performance compared to traditional RL algorithms.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Brandon Rohrer

How K-Nearest Neighbors Works

Explore k-nearest neighbors algorithm for classification and regression, including key considerations like k value, feature scaling, and distance metrics.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Pascal Poupart

Inverse RL

Explore inverse reinforcement learning, its applications, and key concepts like feature matching and maximizing margins. Learn implementation techniques and policy induction.

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

Machine Learning: Clustering & Retrieval

5 ratings

Explore similarity-based algorithms for document retrieval, clustering techniques, and topic modeling. Learn to implement k-nearest neighbors, k-means, EM, and LDA for analyzing large text datasets.

• Coursera
• 17 hours 21 minutes
• Self-Paced
• Paid Course

Paul G. Allen School

Natural Language Processing - Yoav Artzi - Cornell University

Explore natural language instruction following for robots and collaborative scenarios, featuring new datasets, interpretable models, and learning methods combining supervised and reinforcement approaches.

• YouTube
• 1 hour 5 minutes
• Self-Paced
• Free Video

IEEE

Grammatical Inference and Language Frameworks for LANGSEC

Explore grammatical inference and language frameworks for enhancing security protocols and parsers. Learn about pattern languages, formal systems, and their applications in HTTP session analysis.

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

INI Seminar Room 2

Supervised Graph Prediction: From Kernel to Optimal Transport Losses

Explore supervised graph prediction with Prof. Florence d'Alché-Buc, covering kernel and optimal transport losses in this mathematical sciences seminar at the Isaac Newton Institute.

• YouTube
• 1 hour 16 minutes
• Self-Paced
• Free Video

Yannic Kilcher

HyperTransformer - Model Generation for Supervised and Semi-Supervised Few-Shot Learning

Explore HyperTransformer, a novel approach to few-shot learning using transformers to generate CNN weights. Learn about its architecture, advantages, and potential applications in machine learning.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Self-Supervised Learning - The Dark Matter of Intelligence

Explore self-supervised learning as a promising approach for AI to develop background knowledge and common sense, with insights from Yann LeCun and Ishan Misra of Facebook AI Research.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Alfredo Canziani

Supervised and Self-Supervised Transfer Learning With PyTorch Lightning

Explore supervised and self-supervised transfer learning techniques using PyTorch Lightning, comparing their implementation, training, and generalization capabilities in deep learning applications.

• YouTube
• 1 hour 11 minutes
• Self-Paced
• Free Video

Machine Learning with scikit-learn

14 ratings at DataCamp

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Artificial Intelligence (AI) Concepts in Python

16 ratings at DataCamp

Learn the fundamentals behind AI and Machine Learning.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Machine Learning Fundamentals in Python

Learn the art of Machine Learning and come away as a boss at prediction, pattern recognition, and the beginnings of Deep and Reinforcement Learning.

• DataCamp
• 16 hours
• Self-Paced
• Free Trial Available

Machine Learning for Business

Understand the fundamentals of Machine Learning and how it's applied in the business world.

• DataCamp
• 2 hours
• Self-Paced
• Free Trial Available

Fraud Detection in Python

Learn to detect fraud using Python, applying supervised and unsupervised learning techniques, handling imbalanced datasets, and implementing practical models with hands-on experience and real-life insights.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available