Modeling Hotel Bookings in R Using Tidymodels and Recipes
Learn to predict hotel bookings using R, tidymodels, and recipes. Explore data, create visualizations, and build decision tree models to gain insights into booking patterns and improve predictions.
How to Achieve Comprehensive Protection for Cloud Native Applications and Workloads
Learn comprehensive protection strategies for cloud-native applications, covering web security, runtime risks, and predictive models. Explore practical solutions and benefits through demos and real-world examples.
Playing with Games Lifetime Value - Calculation and Best Practices
Explore top game developers' LTV calculation methods, including business model segmentation, resource utilization, and best practices for practical implementation and improvement.
Machine Learning Foundations - Logistic Regression
Master logistic regression fundamentals through hands-on practice, exploring core concepts, implementation techniques, and practical applications in machine learning and data analysis.
Introduction to Decision Trees in Machine Learning - Lecture 3
Explore the fundamentals of decision trees in machine learning, understanding their structure, functionality, and representational capabilities in data-driven decision making.
Feature Engineering and Modeling Techniques for Menu Design at HelloFresh
Discover how HelloFresh uses data-driven approaches to optimize menu design, predict recipe performance, and align product offerings with customer preferences and business strategies.
Utilizing the Power of Machine Learning in Healthcare - Heart Failure Prediction
Explore machine learning's potential in healthcare through heart failure prediction, addressing diagnostic limitations and leveraging neural networks with real-world data.
Build a Neural Network with Python Tutorial - Deep Learning with PyTorch
Learn to build, train, and evaluate a neural network for weather prediction using PyTorch. Covers data preprocessing, model architecture, GPU acceleration, and practical applications.
XGBoost in Python from Start to Finish
Comprehensive guide to implementing XGBoost in Python, covering data preparation, model building, and optimization techniques for effective machine learning applications.
Gradient Boost Part 2 - Regression Details
Detailed exploration of Gradient Boost algorithm for regression, covering initialization, tree building, residual calculation, and prediction optimization. Ideal for those seeking in-depth understanding of this popular machine learning technique.
Predictive Modeling in R With Tidymodels and NFL Attendance
Learn to train predictive models using R's tidymodels framework and NFL game attendance data. Explore data splitting, model building, evaluation, and results visualization techniques.
Tune and Evaluate a Multiclass Lasso Model for NBER Working Papers
Explore multiclass lasso modeling for economic research papers, covering data analysis, feature engineering, model tuning, and evaluation using #TidyTuesday data.
Tune XGBoost With Early Stopping to Predict Shelter Animal Status
Learn to optimize xgboost models using early stopping techniques for predicting animal shelter outcomes, featuring practical examples and efficient coding strategies.
Tune and Evaluate a Decision Tree Model with Scooby Doo Episodes
Learn to predict Scooby Doo monster status using decision trees. Explore model tuning, visualization, and simplification techniques with Julia Silge's practical demonstration.
Model Canadian Wind Turbine Capacity With Decision Trees and Tidymodels
Learn to predict Canadian wind turbine capacity using decision trees and tidymodels, with hands-on data preparation, model tuning, and result interpretation techniques.
Machine Learning Methods for Climate Science - Lecture 4
Explore machine learning applications in climate science, focusing on advanced techniques for analyzing and predicting complex atmospheric and oceanic processes.
Machine Learning Methods for Climate Science - Lecture 3
Explore machine learning applications in climate science, focusing on advanced techniques for analyzing and predicting complex atmospheric and oceanic processes.
Prediction and Planning Under Uncertainty
Explore prediction and planning in uncertain environments, covering model predictive control, stochastic scenarios, and practical applications in AI decision-making.
Joint Embedding Method and Latent Variable Energy Based Models
Explore joint embedding methods and latent variable energy-based models with Yann LeCun. Learn about predictive systems, inference, probabilistic models, and various training techniques for advanced machine learning applications.
Learning with Small Data - Part 2
Explore machine learning techniques for small datasets, combining domain knowledge with ML models to enhance predictions and leverage unlabeled data for improved accuracy.
Offline Reinforcement Learning and Model-Based Optimization
Explore offline reinforcement learning and model-based optimization techniques, focusing on data-driven approaches, Q-function lower bounds, and model inversion networks for automated decision-making.