Fit and Predict With Logistic Regression for Bird Bath Observations in Australia
Learn to model bird bath observations in Australia using tidymodels, combining model specifications with feature engineering recipes for effective fitting and prediction.
Use Tidymodels Workflowsets to Predict Human-Computer Interactions from Star Trek
Explore machine learning with tidymodels workflowsets to predict speaker type in Star Trek interactions, featuring data preprocessing, model evaluation, and result analysis.
Predict Housing Prices in Austin TX with Tidymodels and XGBoost
Explore feature engineering for boosted tree models using Austin housing data. Learn to incorporate text information as indicator variables and tune models for accurate price predictions.
AutoML Toolkit Deep Dive - Automating Feature Engineering and Model Optimization
Explore Databricks Labs' AutoML Toolkit for automating feature engineering, model selection, tuning, and deployment. Learn to accelerate machine learning workflows and enhance productivity.
Augmenting Machine Learning with Databricks Labs AutoML Toolkit
Explore how Databricks Labs AutoML Toolkit simplifies and optimizes machine learning processes, from data preparation to model optimization, using financial loan risk data examples.
How to Handle High Cardinality Predictors for Data on Museums in the UK
Explore effect and likelihood encodings for high cardinality predictors in tidymodels, using UK museum data to demonstrate practical applications.
Deploy Different Prediction Types for a Bigfoot Sighting Model
Learn to deploy various prediction endpoints for a Bigfoot sighting model using vetiver, including feature engineering, logistic regression, and natural language processing techniques.
EvalML AutoML Library to Automate Feature Engineering, Feature Selection, Model Creation and Tuning
Explore EvalML, an AutoML library automating feature engineering, selection, model creation, and tuning. Learn to streamline your data science workflow and improve project efficiency.
Scaling Data and ML with Apache Spark and Feast - Feature Engineering for Production
Explore scaling data and ML with Apache Spark and Feast, focusing on feature engineering challenges and solutions for big data-driven machine learning in production environments.
Interpretable Machine Learning Use-Cases - Kaggle 30 Days of ML Day 15
Explore machine learning explainability, interpretability, and use cases for model insights. Learn about feature importance, debugging, and building trust in AI systems.
Self-serve Feature Engineering Platform Using Flyte and Feast
Explore a self-serve feature engineering platform leveraging Flyte and Feast technologies, enhancing data science workflows and machine learning model development.
Automating Supervised Machine Learning Pipeline Development
Explore methodologies for automating supervised machine learning pipelines, covering data cleaning, feature engineering, and model development. Gain insights from experienced data scientists on best practices and techniques.
Step-by-Step Machine Learning Project Implementation: EDA, Feature Engineering, Selection, and Model Training
Comprehensive guide to implementing a machine learning project, covering data cleaning, EDA, feature engineering, selection, model training, and hyperparameter tuning.
Building Robust and Scalable Recommendation Engines for Online Food Delivery
Unlock the power of recommendation engines for food delivery. Learn data preprocessing, feature engineering, and scalable algorithms to create personalized recommendations using the DHRD dataset.
Mastering Multilingual Recommender Systems - Grandmaster Series
Explore NVIDIA Merlin team's winning approach in Amazon KDD Cup 2023, featuring innovative techniques for multilingual e-commerce product recommendations and next product title generation.
Combatting Human Trafficking with Anomaly Detection - Part 1
Explore anomaly detection techniques to combat human trafficking through financial data analysis. Learn to leverage transaction patterns and industry indicators for effective detection.
Automated Machine Learning on Azure - Comparing Azure ML Service and Azure Databricks
Explore AutoML on Azure Machine Learning Service and Azure Databricks to quickly build and deploy ML models, reducing time and expertise required for preprocessing and model creation.
Navigating the Skies of Data: A Flight Through Feature Stores
Explore Feature Stores' role in streamlining ML projects, acting as a control tower for efficient feature management in data processes.
Cosine Similarity - Introduction to Text Analytics with R
Explore cosine similarity in text analytics: its mathematical basis, application in document comparison, and effectiveness in feature engineering for R-based text analysis.
Hands-on with R: Regression Modeling and Feature Selection - Part 5
Fit regression models to temperature data, apply stepwise variable selection, and choose the best model using out-sample RMSE. Enhance skills in feature engineering and dimension reduction.
Modeling GDPR Violations in R With Tidymodels
Explore data preprocessing and modeling for GDPR violations using tidymodels in R. Learn feature engineering, workflow creation, and prediction techniques with real-world data.