Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Optimization in Machine Learning: Gradient Descent
Graph Chatbot
Related lectures (51)
Feature Engineering: Polynomial Regression
Log in to Mediaspace to watch this video
Covers fitting linear regression on features of the original predictors for flexible feature representation.
Cross-Validation: Techniques and Applications
Log in to Mediaspace to watch this video
Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.
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 Regression: Foundations and Applications
Log in to Mediaspace to watch this video
Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.
Neural Networks: Basics and Applications
Log in to Mediaspace to watch this video
Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
Deep Learning: Multilayer Perceptron and Training
Log in to Mediaspace to watch this video
Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Feature Engineering: Missing Data and Standardization
Log in to Mediaspace to watch this video
Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.
Linear Regression: Basics and Gradient Descent
Log in to Mediaspace to watch this video
Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.
Regression Trees and Ensemble Methods in Machine Learning
Log in to Mediaspace to watch this video
Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
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.
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.
Kernel Methods in Machine Learning: Kernel Regression and SVM
Log in to Mediaspace to watch this video
Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.
Overfitting in Supervised Learning: Case Studies and Techniques
Log in to Mediaspace to watch this video
Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.
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.
Understanding Data Attributes
Log in to Mediaspace to watch this video
Covers the analysis of various data attributes and linear regression models.
Regression: Interactive Lecture
Log in to Mediaspace to watch this video
Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.
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.
Polynomial Regression and Gradient Descent
Log in to Mediaspace to watch this video
Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.
Machine Learning: Supervised and Unsupervised Learning Techniques
Log in to Mediaspace to watch this video
Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Machine Learning Applications: Regression and Classification
Log in to Mediaspace to watch this video
Explores machine learning applications in materials modeling, covering regression, classification, and feature selection.
Previous
Page 2 of 3
Next
