Logistic Regression - Continued
Explore advanced concepts and applications of logistic regression for enhanced predictive modeling and data analysis skills.
Industrializing Machine Learning Workflows in Drug Discovery
Discover how machine learning workflows accelerate drug discovery through automated predictive modeling, data management, and cloud computing for evaluating compound properties and safety.
내가 왕이 될 콘텐츠인가 - 콘텐츠 흥행 예측과 AI 기반 분석
Discover how SK Broadband combines AI with domain expertise to predict content success, minimizing investment risks through objective metrics and machine learning models for entertainment content performance forecasting.
Fraud Models in the Energy Sector - Using AI for Smart Meter Theft Detection
Explore AI-driven fraud detection for smart meters in the energy sector, focusing on energy theft prevention and consumption monitoring techniques.
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.
Can Data-Centric AI Help Us Develop Better Drugs?
Explore how machine learning and high-quality data can revolutionize drug development, predicting outcomes and designing safer, more effective therapies faster and at lower costs.
Getting Started with SparkML in Microsoft Fabric
Explore end-to-end machine learning in Microsoft Fabric using Spark notebooks. Learn to build models and make predictions with Fabric datasets in this practical demonstration.
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.
Predictive Modelling
Explore predictive modeling techniques and applications for data-driven decision-making and forecasting in various domains.
AIrsenal - Machine Learning for Fantasy Premier League
Explore machine learning in fantasy football with AIrsenal, a project using AI to manage Fantasy Premier League teams. Learn about its development and potential applications.
Machine Learning for Scent
Explore machine learning applications in digitizing scent, from understanding olfactory processes to predicting odor descriptors using graph neural networks and interpreting molecular fragrance data.
Overview of Machine Learning Approaches at GSK.ai
Explore machine learning approaches at GSK.ai, focusing on innovative techniques and applications in pharmaceutical research and development.
Feature Selection for Machine Learning
Explore feature selection methods for machine learning, including filter, wrapper, and embedded approaches. Learn to choose the most predictive variables for production-ready models.
Dynamic Huff's Gravity Model with Covariates for Site Visitation Prediction
Discover how enhanced Huff's Gravity Model incorporates mobility and behavioral data to accurately predict consumer location visits, with practical applications for retail and urban planning.
Retooling Supervised Machine Learning for Data-Driven Hydrothermal Resource Assessments
Explore machine learning approaches for unbiased geothermal resource favorability mapping, improving predictive performance and confidence in resource estimates.
A City Digital Twin for Sustainable Mobility: Big Data Analytics and Predictive Models
Explore big data analytics and predictive models for sustainable urban mobility using digital twin technology.
Calling the Shot: How AI Predicted Fusion Ignition Before It Happened
Explore how AI predicted fusion ignition at the National Ignition Facility, revolutionizing clean energy research and bringing us closer to harnessing the power of the stars.
Machine Learning Applications in Scientific Problems with Sparse Data - Afternoon Address
Explore Dr. Kelli Humbird's career in data science, focusing on inertial confinement fusion and machine learning applications in scientific problems with sparse data.
Introduction to Machine Learning: Regression - Part 1
Explore regression in machine learning: estimate relationships, predict outcomes, and apply algorithms for continuous target variables. Learn evaluation techniques and practical considerations.
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.
Scaling Paid Marketing with Automation and Predictive Models
Discover strategies for scaling paid marketing through automation and predictive modeling, enhancing budget planning and optimization for improved marketing efficiency.