Opioid Use Disorder Prediction Using AI and Existing Risk Models
Comparative study on AI models for predicting opioid use disorder, showcasing superior performance of transformer-based AI over existing clinical tools and other machine learning approaches.
Statistics and Machine Learning in R
If you want to do statistics and machine learning in R, this playlist is for you, and you only. No one else is allowed to watch it.
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.
Intro to Machine Learning
Dive into machine learning fundamentals with engaging videos covering models, data exploration, validation, and advanced concepts like random forests and AutoML.
From Smart Betas to Smart Alphas - Quantum Mental in the Age of Machine Learning
Explore smart beta and alpha strategies in finance with Milind Sharma, covering machine learning, deep learning, and portfolio construction techniques for modern quantitative investing.
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.
Marketing & Retail Analytics
Comprehensive tutorial on marketing analytics, covering RFM analysis, clustering, churn prediction, market basket analysis, and CLV modeling using tools like Tableau and KNIME.
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.
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.
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.
How to Use Random Forest in Python
Learn to implement Random Forest in Python for data analysis, including data importing, feature engineering, and model fitting techniques.
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 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।
Predict Water Availability in Sierra Leone With Random Forests
Explore random forest modeling with tidymodels to analyze water sources in Sierra Leone, covering data preparation, model building, and evaluation techniques.
Topological Data Analysis Based Machine Learning Models for Drug Design
Explores TDA-based models for effective molecular representation in drug design, combining persistent homology, spectral models, and Ricci curvature with machine learning for improved performance.
Empowering the Data Science Ecosystem for Whole Genome Sequencing Analysis Using ML Methods and Tools
Explore advanced methods for analyzing whole genome sequencing data using machine learning and ensemble techniques. Learn to leverage biobanks and electronic health records for powerful genetic insights.
Invariance, Causality and Novel Robustness
Explore invariance, causality, and robustness in high-dimensional statistics. Learn about modern applications, heterogeneous data, and novel approaches to prediction problems using causal inference techniques.
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.
The Arboreal Gas - Percolative Properties of Random Forests
Explore the percolative properties of random forests in the arboreal gas model, examining the formation of giant trees and its applications to branched polymer gelation.
Machine Learning Applications in Healthcare - Saudi Arabian Hospital Case Studies
Explore real-world applications of machine learning in Saudi Arabian healthcare through case studies focusing on breast cancer classification and predictive modeling techniques.
Machine Learning Fundamentals - Lecture 8
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.