SVM and Multiclass Classification

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (39)

Multi-Class Classification: Approaches and Boundaries Log in to Mediaspace to watch this video

Explains the strategies for multi-class classification and the concept of decision boundaries.

Linear Models for Classification: Multi-Class Extensions Log in to Mediaspace to watch this video

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Linear Models for Classification: Logistic Regression and SVM Log in to Mediaspace to watch this video

Covers linear models for classification, focusing on logistic regression and support vector machines.

Supervised Learning: Classification Algorithms Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Gaussian Naive Bayes & K-NN Log in to Mediaspace to watch this video

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Support Vector Machines: Interactive Class Log in to Mediaspace to watch this video

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Classification Algorithms: Generative and Discriminative Approaches Log in to Mediaspace to watch this video

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Linear Models: Classification Basics Log in to Mediaspace to watch this video

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Recommender Systems: Text Classification & Naïve Bayes Log in to Mediaspace to watch this video

Explores text classification using Naïve Bayes in content-based recommenders.

Support Vector Machines: Basics and Applications Log in to Mediaspace to watch this video

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Document Classification Log in to Mediaspace to watch this video

Explores document classification methods, including Naïve Bayes and word embeddings.

Untitled Log in to Mediaspace to watch this video

Machine Learning Fundamentals: Structure Discovery, Classification, Regression Log in to Mediaspace to watch this video

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Binary Classification by Regression: Decision Functions and Cost Functions Log in to Mediaspace to watch this video

Explores binary classification by regression, decision functions, and various cost functions.

Feature Selection, Kernel Regression, Neural Networks Playground Log in to Mediaspace to watch this video

Covers feature selection, kernel regression, and neural networks through exercises.

Receiver-Operator Characteristics: ROC Curves Log in to Mediaspace to watch this video

Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion Log in to Mediaspace to watch this video

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Decision Trees: Classification Log in to Mediaspace to watch this video

Introduces decision trees for classification, covering entropy, split quality, Gini index, advantages, disadvantages, and the random forest classifier.

Model Assessment: Metrics and Selection Log in to Mediaspace to watch this video

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Page 2 of 2