Complete Machine Learning in 6 Hours - Krish Naik
Comprehensive machine learning tutorial covering key algorithms, techniques, and practical implementations for regression, classification, clustering, and ensemble methods.
Machine Learning From Scratch in Python - Full Course With 12 Algorithms
Comprehensive Python implementation of 12 popular machine learning algorithms from scratch, covering KNN, regression, SVM, decision trees, random forests, PCA, K-means, AdaBoost, and LDA.
Finding the Tallest Tree - Comparing Tree-Based Models
Explore various tree-based models in R, comparing decision trees, random forests, and boosted trees. Learn about different packages, their implementation, and evaluate models based on user-friendliness, accuracy, and speed.
Do Simpler Models Exist and How Can We Find Them? - Cynthia Rudin
Explore simpler, interpretable machine learning models and their advantages in criminal justice and beyond. Learn about the Rashomon set, decision trees, and strategies to combat overfitting.
Introduction to Interpretable Machine Learning II - Cynthia Rudin
Explore interpretable machine learning with a focus on decision trees, information theory, and analytical bounds. Gain insights into modern approaches and practical applications.
Introduction to Interpretable Machine Learning - Cynthia Rudin
Explore interpretable machine learning with Cynthia Rudin, covering classification, natural language processing, and decision trees. Gain insights into model complexity and information theory applications.
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.
Mitigating Backdoors in Machine Learning Models - Security and Defense Strategies
Explore techniques for defending against undetectable backdoors in machine learning systems through black-box mitigation strategies, focusing on binary classification and regression models.
Classification Algorithm in Hindi
वर्गीकरण एल्गोरिदम की व्यापक समझ: लिनियर रिग्रेशन से लेकर सपोर्ट वेक्टर मशीन तक, सैद्धांतिक अवधारणाओं और पायथन में कार्यान्वयन के साथ।
Machine learning en espanol
Introducción práctica al aprendizaje automático en español, cubriendo técnicas supervisadas y no supervisadas con aplicaciones reales como reconocimiento de imágenes y sistemas de recomendación.
Machine Learning Tutorials for Beginners Using Python in Hindi
हिंदी में पायथन का उपयोग करके मशीन लर्निंग की मूल बातें सीखें। लीनियर रिग्रेशन, सपोर्ट वेक्टर मशीन और डिसीजन ट्री जैसी तकनीकों को समझें और कोड करें।
Machine Learning in Hindi - Python for Machine Learning
मशीन लर्निंग की मूल अवधारणाओं और पायथन के उपयोग को समझें। पांडास, नंपी, मैटप्लॉटलिब जैसे लाइब्रेरीज़ और लीनियर रिग्रेशन, लॉजिस्टिक रिग्रेशन, डिसीजन ट्री जैसे एल्गोरिदम सीखें।
Machine Learning Full Course in Hindi
मशीन लर्निंग की मूल अवधारणाओं और एल्गोरिदम का व्यापक हिंदी परिचय, जिसमें लीनियर रिग्रेशन, लॉजिस्टिक रिग्रेशन, डिसीजन ट्री, रैंडम फॉरेस्ट, नेइव बेयस और SVM शामिल हैं।
Improved Feature Importance Computation for Tree Models Based on the Banzhaf Value
Explore advanced feature importance computation for tree models using the Banzhaf value, offering faster algorithms and improved numerical robustness compared to Shapley value-based methods.
Decision Trees - A Friendly Introduction
Learn to build and train decision trees through a simple example, exploring key concepts like data splitting, overfitting prevention, and alternative evaluation metrics.
Partial Dependence Plots for Mario Kart World Records
Explore decision tree modeling and partial dependence profiles for Mario Kart world records using tidymodels and DALEX, with a focus on predicting shortcut usage.
Fairness Without Imputation: A Decision Tree Approach for Fair Prediction With Missing Values
Explore a novel decision tree approach for fair prediction with missing data, addressing fairness concerns without relying on imputation techniques.
Introduction to Decision Trees - Gini Impurity and Python Code
Learn decision tree fundamentals, including Gini impurity and Python implementation, through a beginner-friendly introduction with concrete examples and code for sepsis survival prediction.
Singular Value Decomposition with R - Introduction to Text Analytics with R Part 8
Learn to perform truncated SVD, project TF-IDF vectors into semantic space, compare decision tree and random forest models, and explore random forest tuning using R packages like irlba and caret for text analytics.
Classification Trees in Python from Start to Finish
Comprehensive guide to building and optimizing classification trees in Python, covering data preparation, model construction, pruning techniques, and visualization.
Building Our First Model - Introduction to Text Analytics with R Part 4
Learn to build and evaluate a text classification model using R, covering tokenization, cross-validation, parallel processing, and decision tree training for optimal accuracy in text analytics.