Applied Machine Learning 2019 - Lecture 07 - Linear Models for Classifications, SVMs Published 2019-02-13 Download video MP4 360p Recommendations 18:26 The most important ideas in modern statistics 12:09 Inverse matrices, column space and null space | Chapter 7, Essence of linear algebra 20:27 Regularization Part 1: Ridge (L2) Regression 06:28 Principal Component Analysis (PCA) 31:23 Concurrency is not Parallelism by Rob Pike 46:51 Lecture 2. Introduction to Instruments and Musical Genres 12:45 Hash Tables, Associative Arrays, and Dictionaries (Data Structures and Optimization) 16:37 Recurrent Neural Networks (RNNs), Clearly Explained!!! 1:15:15 Applied ML 2020 - 11 - Model Inspection and Feature Selection 09:27 Bootstrapping Main Ideas!!! 15:55 Visualize Spectral Decomposition | SEE Matrix, Chapter 2 16:12 Word Embedding and Word2Vec, Clearly Explained!!! 12:51 Change of basis | Chapter 13, Essence of linear algebra 12:51 Singular Value Decomposition (SVD): Mathematical Overview 09:03 Gemini 1.5: Google's Latest AI Challenging OpenAI's GPT-4 16:46 Abstract vector spaces | Chapter 16, Essence of linear algebra 12:52 OpenAI changed AI Video FOREVER | Full Sora Review (All Features) Similar videos 1:15:59 Applied Machine Learning 2019 - Lecture 06 - Linear Models for Regression 09:34 How Support Vector Machine (SVM) Works Types of SVM Linear SVM Non-Linear SVM ML DL by Mahesh Huddar 1:09:32 Applied Machine Learning 2019 - Lecture 15 - Clustering and Mixture models 22:39 Feature Selection In Machine Learning | Feature Selection Techniques With Examples | Simplilearn 1:20:34 Machine Learning 1 - Linear Classifiers, SGD | Stanford CS221: AI (Autumn 2019) 1:17:05 Applied Machine Learning 2019 - Lecture 12 - Model Interpretration and Feature Selection 1:14:41 Applied Machine Learning 2019 - Lecture 02 - Version control and Testing 1:22:07 Applied Machine Learning 2019 - Lecture 13 - Parameter Selection and Automatic Machine Learning 1:53:44 Stanford CS229: Machine Learning | Summer 2019 | Lecture 7 - GDA, Naive Bayes & Laplace Smoothing 1:02:06 Lecture 3: Linear Classifiers 52:16 Yet Again: R + Data Science: Lecture 7 - Linear Models in R 1:48:37 Stanford CS229: Machine Learning | Summer 2019 | Lecture 16 - K-means, GMM, and EM 05:50 Linear Classification - An visual explanation (2021) 1:17:55 Applied Machine Learning 2019 - Lecture 04 - Introduction to supervised learning 1:20:25 Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) More results