Data Mining
Comprehensive exploration of data mining techniques, covering preprocessing, association rules, classification methods, and clustering algorithms for extracting valuable insights from datasets.
Machine Learning with Python
Comprehensive Python tutorial covering machine learning concepts, algorithms, and deep learning with TensorFlow. Includes hands-on projects and real-world applications.
Tabular Data
Learn machine learning techniques for tabular data, including model evaluation, feature engineering, tree-based models, hyperparameter tuning, and neural networks using AWS SageMaker.
E2EML - How Selected Models and Methods Work
Explore key machine learning algorithms and methods, including decision trees, Bayes Theorem, autocorrelation, Support Vector Machines, and k-nearest neighbors, in this comprehensive video series.
Machine Learning Fun and Easy
Explore popular machine learning concepts through engaging lectures covering regression, clustering, neural networks, and more. Gain fundamental knowledge in an accessible, enjoyable format.
Application of Classification Algorithms
Explore popular classification algorithms, their comparisons, and real-world applications through a case study on employee promotion prediction using various techniques.
Python Machine Learning & AI Mega Course - Learn 4 Different Areas of ML & AI
Comprehensive exploration of machine learning and AI in Python, covering algorithms, neural networks, chatbots, and game AI. Hands-on projects and in-depth explanations for practical skills development.
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.
Ultimate Machine Learning Projects with Python for Beginners - 2024
Master Python-based machine learning through hands-on projects, from basic algorithms to advanced applications like fake news detection, predictive maintenance, and emotion recognition. Build a portfolio with real-world implementations.
Machine Learning and Neural Networks: K-Nearest Neighbors - Day 9
Explore K-Nearest Neighbors algorithm for machine learning and neural networks, covering its principles, implementation, and applications in classification and regression tasks.
On the Robustness of Deep K-Nearest Neighbors
Explore the robustness of Deep k-Nearest Neighbors against adversarial attacks, proposing a heuristic method to find vulnerabilities in this defense mechanism for deep learning models.
CS480-680 - K-Nearest Neighbours
Learn about k-nearest neighbours algorithm for classification and regression, including examples, machine learning concepts, and accuracy considerations.
Machine Learning Approaches for Predicting Seismic, Acoustic and Atmospheric Data
Explore machine learning techniques for predicting seismic, acoustic, and atmospheric data, focusing on KNN and neural networks with uncertainty quantification for improved accuracy.
Vector and Keyword Search for Enhanced LLM Performance
Explore hybrid search techniques combining keyword and vector algorithms to enhance LLM performance, including metadata filtering, TF/IDF ranking, and advanced vector search methods.
K-Nearest Neighbors (KNN) Algorithm - Introduction and Applications
Explore k-Nearest Neighbors algorithm: concepts, applications, assumptions, implementation, and optimization techniques for effective machine learning.
K-Nearest Neighbors Implementation in R
Implement K-Nearest Neighbors algorithm in R programming language. Learn practical application of this machine learning technique for classification and regression tasks.
Neo4J GDS: Node Embeddings Algorithms for Data Science and Machine Learning
Explore node embedding algorithms in Neo4J GDS for data science and machine learning, creating powerful graph-based representations.
Introduction to Machine Learning: Classification Techniques - Part 2
Explore classification techniques in machine learning, including logistic regression and KNN, with practical insights from industry experts.
Revisiting Nearest Neighbors from a Sparse Signal Approximation View
Explore nearest neighbor methods from a sparse signal approximation perspective, uncovering improved neighborhood definitions and their applications in graph-based signal processing and machine learning.
The Quest for Adaptivity in Machine Learning - Comparing Popular Methods
Explore supervised learning algorithms' strengths and weaknesses, comparing k-nearest-neighbor, kernel methods, and neural networks for adaptivity and performance across various problems.
Machine Learning for Predicting Additive Manufacturing Part Quality - Tutorial on Support Vector Regression
Learn to predict additive manufacturing part quality using support vector regression. Explore data analysis, hyperparameter tuning, and cross-validation techniques for machine learning in 3D printing applications.