Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 3 - predictors
Stanford CS230 | Autumn 2025 | Lecture 3: Full Cycle of a DL project
MIT: Machine Learning 6.036, Lecture 3: Features (Fall 2020)
Machine Learning course- Shai Ben-David: Lecture 3
Stanford CS231N | Spring 2025 | Lecture 3: Regularization and Optimization
Lecture 3: Linear Classifiers
Lecture 3 | Machine Learning (Stanford)
Optimization - Lecture 3 - CS50's Introduction to Artificial Intelligence with Python 2020
Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 3 - Tranformers & Large Language Models
mlcourse.ai. Lecture 3. Decision trees. Part 2. Practice
Backpropagation, intuitively | Deep Learning Chapter 3
Stanford CS229: Machine Learning | Summer 2019 | Lecture 3 - Probability and Statistics
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 3 - Model-Free Policy Evaluation
Advanced Algorithms (COMPSCI 224), Lecture 3
Machine Learning for Physicists (Lecture 3): Training networks, Keras, Image recognition
Machine Learning course- Shai Ben-David: Lecture 2
Lecture 3 | Loss Functions and Optimization
Lecture 03 -The Linear Model I
Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)
