Public Health Data Sources for Machine Learning and Examples of Research
Explore public health data sources for machine learning and discover research examples in this informative talk. Learn how to leverage data for innovative health studies and applications.
ML and Health Care - An Obgyn Perspective by Uma Ram
Explore machine learning applications in obstetrics and gynecology from an expert's perspective, focusing on potential benefits and challenges in women's healthcare.
Digital Twins in Healthcare by Anurag Agrawal
Explore the potential of digital twins in healthcare, focusing on their applications, benefits, and impact on patient care, medical research, and healthcare systems optimization.
How to Train Your DragoNN - A Primer on Deep Learning for Regulatory
Explore deep learning techniques for regulatory genomics, focusing on key benefits and applications in analyzing genomic data for improved understanding of gene regulation.
Introduction to Probability, Statistics, Statistical Testing
Explore fundamental concepts of probability, statistics, and statistical testing, essential for understanding data analysis and machine learning in health and disease research.
Data- Access, Analysis and Distillation by K VijayRaghavan
Explore data access, analysis, and distillation techniques for machine learning in healthcare. Learn to harness computational methods for understanding health-related data across multiple domains.
Heterogeneity of Type-2 Diabetes in India - I by V Mohan
Explore the diverse manifestations of Type-2 Diabetes in India with Dr. V Mohan, gaining insights into its unique characteristics and implications for diagnosis and treatment in the Indian population.
Sample Complexity of Estimation in Logistic Regression - Lecture
Explore sample complexity in logistic regression, examining parameter estimation across temperature regimes. Gain insights into non-asymptotic analysis and critical points in the complexity curve.
Building a Predictive Pipeline to Rapidly Detect Phishing Domains
Explore techniques for building a predictive pipeline to detect phishing domains using machine learning, focusing on data acquisition, algorithm selection, and performance metrics.
Duplicating Black Box Machine Learning Models
Exploración de técnicas avanzadas para duplicar modelos de aprendizaje automático de caja negra, destacando vulnerabilidades en servicios en la nube y proponiendo un nuevo método de ataque más eficiente.
Building a Predictive Pipeline to Rapidly Detect Phishing Domains
Learn to build a predictive pipeline using Python and machine learning to detect phishing domains in real-time, leveraging Certificate Transparency logs and supervised learning techniques.
Fast Analytics On Big Data
Explore fast analytics on big data using an open-source platform for in-memory distributed data processing, enabling efficient predictive modeling and analytics on large-scale datasets.
Power of Electric Snakes! PySpark for Big Data and Data Science
Learn to harness PySpark for big data analytics, covering RDDs, DataFrames, transformations, and machine learning. Gain practical skills for efficient data processing and analysis using Python and Spark.
Deep Learning with TensorFlow
Explore TensorFlow's fundamentals and create deep learning models through interactive discussions. Gain practical skills to develop and implement AI in your applications.
Logistic Regression - Continued
Explore advanced concepts and applications of logistic regression for enhanced predictive modeling and data analysis skills.
Identifying Biomarkers of Cardiovascular Diseases with Machine Learning
Explore machine learning techniques for identifying cardiovascular disease biomarkers, enhancing early diagnosis and prevention strategies using UK longitudinal study data.
Introduction to Logistic Regression
Learn about binary classification, logistic regression, and related concepts like probit and logit links in this concise introduction to fundamental statistical modeling techniques.
Effect of Feature Engineering in Logistic Regression
Explore feature engineering's impact on logistic regression, covering sigmoid curves, non-monotonic relationships, and sinusoidal patterns.
Logistic Regression: Introduction to Binary Classification and Linear Classifiers
Master logistic regression for binary classification, covering linear classifiers, sigmoid functions, parameter estimation, and log-likelihood functions.
Logistic Regression Implementation in R for Automotive Crash Testing
Implement logistic regression in R for automotive crash testing, from data preparation to model evaluation and prediction.
Logistic Regression to Deep Learning Neural Network
Explore the evolution from logistic regression to deep neural networks, understanding key concepts and applications in machine learning and AI.