Machine Learning
Comprehensive Hindi course covering machine learning fundamentals, algorithms, and practical implementations using Python, with real-world examples and solved problems.
Few-Shot Chest X-Ray Diagnosis Using Clinical and Literature Images
Explore innovative few-shot learning methods for chest X-ray diagnosis using clinical and literature images, enhancing radiology image analysis with advanced machine learning techniques.
Random Forests
Learn to build, evaluate, and implement Random Forests, including handling missing data and clustering. Gain practical skills using R for this powerful machine learning technique.
Decision Trees and Ensemble Methods
Explore decision trees, ensemble methods, and advanced techniques like random forests and boosting algorithms for improved machine learning model performance.
Ensemble Learning - Decision Tree Algorithm - Gini Index
Master ensemble learning techniques, decision trees, and boosting methods. Hands-on demos with real datasets enhance understanding of these powerful machine learning concepts for improved model performance.
Boosting
Explore ensemble learning techniques like boosting to enhance machine learning performance. Learn about bias, variance, and popular algorithms for improved model accuracy and decision-making.
Bagging and Boosting
Explore ensemble learning techniques like bagging and boosting to improve machine learning model performance. Learn how to combine multiple algorithms for better results across various domains.
Complete Machine Learning Course - From Basics to Advanced Concepts
Comprehensive Hindi guide covering ML fundamentals, supervised/unsupervised learning, reinforcement, ensemble methods, neural networks, and genetic algorithms for exam preparation.
Scientific Text Mining and Knowledge Graphs
Explore scientific text mining and knowledge graphs, focusing on sequence labeling, multi-output models, and ensemble learning for information extraction and table component analysis.
Boosting and Ensembles in Machine Learning - Lecture 19
Dive into advanced boosting and ensemble techniques in machine learning, exploring methods to combine multiple models for improved predictive performance and robustness.
Boosting and Ensemble Methods in Machine Learning - Lecture 18
Dive into boosting algorithms and learn how to combine weak learning rules to create powerful predictive models in machine learning applications.
Machine Learning: Boosting and Ensemble Methods - Lecture 19
Dive into advanced boosting techniques and ensemble methods to transform weak learning algorithms into powerful predictive models for enhanced machine learning capabilities.
Logistic Regression and Ensemble Learning - Bagging and Boosting - AdaBoost
Explore logistic regression, ensemble learning, bagging, and boosting in this AI/ML course. Learn AdaBoost and decision stumps for strong non-linear classification.
Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems
Explore state-of-the-art machine learning techniques and their applications in finance, healthcare, and recommendation systems, focusing on ensemble methods, collaborative filtering, and portfolio construction.
Pre-trained Ensembles for Bayesian Optimization of Protein Sequences
Explore pre-trained ensembles for Bayesian optimization of protein sequences, enabling accurate predictions with minimal data and guiding iterative design for novel peptide inhibitors.
FADE - FAir Double Ensemble Learning for Observable and Counterfactual Outcomes
Learn a novel machine learning approach for fair prediction of both observable and counterfactual outcomes, enhancing decision-making in various domains.
Mem2Img - Memory-Resident Malware Detection via Convolution Neural Network
Innovative framework using convolutional neural networks to detect memory-resident malware, overcoming limitations of traditional antivirus software and handcrafted feature-based approaches.
Boosting and Ensembles in Machine Learning - Lecture 18b
Discover how to transform weak learning algorithms into powerful predictive models through boosting and ensemble techniques in machine learning.
AI-Based Wafer Defect Detection Using Anomaly Detection - Real-Time Implementation
Discover advanced AI techniques for real-time wafer defect detection through unsupervised and semi-supervised learning, focusing on anomaly detection and segmentation in industrial manufacturing environments.
Delivering Delivery Time Prediction - Machine Learning Lessons and Model Optimization
Discover practical insights into ML model development, from handling unclear requirements to ensemble classification techniques and determining optimal probability cutoffs for delivery predictions.
Improving Semantic Segmentation - U-Net Performance via Ensemble of Multiple Trained Networks
Enhance U-Net performance for semantic segmentation using ensemble methods with ResNet34, Inception V3, and VGG16 networks. Includes code, dataset, and practical implementation tips.