AutoML Platforms: Strengths and Pitfalls
AutoML can speed up experimentation and raise baselines, but it is not a silver bullet. Know when to…
Neural Nets for Demand Forecasting
Neural networks can capture nonlinearity and complex seasonality that trip up simple baselines.…
MLOps Basics: From Notebook to Production
Ship models with confidence by treating machine learning like software, not experiments that never…
Ensemble Methods: Bagging, Boosting & Stacking
Boost accuracy by combining weak and strong learners the right way. Understand how bagging,…
Handling Seasonality and Trend in Retail Forecasts
Retail demand swings with holidays, promotions, and calendar quirks. Learn how to encode seasonality…
Prophet vs. ARIMA: Which Fits Your Data?
Compare model assumptions and features to pick the best forecaster for your series. Know when…
Hyperparameter Tuning: Grid, Random or Bayesian?
Compare popular search strategies and when to use each for faster, better models. Avoid overfitting…
Avoiding Data Leakage in Model Training
Spot the subtle ways information from the future can sneak into your training data. Master pipelines…
Interpreting SHAP Values Like a Pro
Learn how SHAP breaks a single prediction into feature contributions you can trust. Understand…
Feature Engineering for Predictive Accuracy
Features translate raw data into signals models can use. This quiz covers lags, aggregations,…
