Machine Learning for Aspiring Data Scientists: Zero to Hero

Explore linear regression for predicting continuous insurance claims in a real-world Insure Me scenario. Learn how X features map to Y claims through training on historical data.

Learn how supervised learning uses true labeled data during training to predict claims from client features, and how inference uses the trained model to estimate future claims quickly.

Explore linear, nonlinear, and proportional relations with real-world examples—car rental thresholds, tax structures, and commissions—and connect these to linear regression concepts and the intercept.

Explore when to use a linear model by weighing assumptions, opportunities for insights from coefficients, and the value of explainability, while noting limits and where transformations may help.

Explore min-max scaling with pandas to scale each feature independently, train a linear regression on the scaled data, and compare outcomes with mean squared error.

Assess the problems of min-max scaling, notably its distortion by outliers and keeping targets unscaled, then consider alternative input scaling approaches for better interpretation.

During inference, scale new inputs with the training data's mean and standard deviation, not row-specific values, and remember these statistics to ensure consistent predictions.

Drive L1 regularized linear regression with varying alphas to zero out some features, discarding phone numbers and house size, while validation seeks a sweet spot to balance error and overfitting.

Learn why evaluating models on training data misleads about performance, and use held-out data to compare regularized and unregularized models, guarding against overfitting and improving generalization.

Split data into training, validation, and test sets; train a pipeline on the training data, scale with the training mean and std, and assess out-of-sample generalization on the validation set.

Use the validation set to perform hyperparameter tuning across model templates, comparing L1 and L2 regularization with different alphas via grid search, to select the best model and estimate generalization.

Design experiments in advance and split data into training and validation sets to minimize being fooled by randomness, guided by the no free lunch theorem and cross-validation to estimate performance.

Use cross-validation with multiple folds (typically five or ten) to train models in Python and average results to reduce variance. Reserve a test set at the end for model selection.

Explore AutoML with h2o, which automates model selection by trying multiple templates, ensembles, and fivefold cross-validated performance, and even generates new features by multiplying feature pairs.

Use feature importance after training to spot data leakage and assess if the model relies on suspicious inputs, like basket size, via linear model coefficients.

Identify look-ahead bias as a common pitfall where future information leaks into predictions. Avoid aggregated time-series features that reveal November prices early, which cheats the model’s training and deployment.

Explore two look-ahead bias solutions: using the previous year's monthly average to capture seasonality, and applying a 30-day rolling average before the purchase date.

Master temporally aware cross-validation for time-series data by training on prior periods and validating on later months with rolling or sliding windows, for robust model selection and tuning.

Learn how predicting purchases does not prove a model understands price sensitivity, and why causal inference and counterfactuals require separate validation before using a model for new tasks.

Learn why binary classification uses a single output and deduces the other class, while multiclass requires K outputs and a self max function to combine probabilities.

In logistic model, there is no closed form solution to obtain optimal parameters; instead evaluate the cross entropy loss for parameter sets with a trial and error approach.

Learn to compute the gradient of the loss as a vector of partial derivatives across all model parameters, guiding the fastest ascent or descent toward optimal weights and biases.

Explore sensitivity and specificity in binary classification, including how many positives and negatives are correctly identified, and the tradeoff with recall, precision, and thresholds.