Data Science for Business Leaders: ML Fundamentals

Define a model as a tool that generalizes training data to produce outputs, noting correlation is not causation, and outline the lifecycle from population of concern to production for predictions.

Explore how machine learning combines models, learning processes, and retraining to automate improvement. Explain business context, model building algorithms, end-to-end learning, and autonomous reinforcement learning.

Compare traditional statistical methods with modern machine learning, highlighting automatic feature selection, train-test splits, and the trade-off between predictive performance and explainability and transparency.

Explore how deep learning, a specialization of machine learning powered by neural networks, handles images, video, and language. See how convolutional networks learn features and biased data mislead the model.

Explore how AutoML accelerates machine learning by automating data gathering, feature engineering, model building, testing, selection, deployment, monitoring, and documentation, while keeping human definition and interpretation in focus.

Define artificial intelligence as a model-driven system with inputs and a capability, illustrated by spam filters and self-driving cars, and contrast static and dynamic learning with data capture.

Identify common machine learning pitfalls, especially overfitting and underfitting, and learn to ensure generalization by using representative training data and proper test and validation sets.

Inspect your business processes to uncover machine learning opportunities. Analyze core and non-core activities—both physical and virtual, manual or automated—to identify where ML adds value.

Explore seven machine learning themes generalized for diverse business contexts, and learn when and how machine learning might fit into a particular business setting.

Analyze how machine learning augments human decisions by considering more inputs, including promotions and local events, to predict store foot traffic and optimize staffing and planning.

Explore how machine learning predicts people across contexts like customers or patients. Consider ethical concerns, fairness, and accountability—would you stand behind your model's decisions in practice.

Explore how AI capabilities enable automation and improved decision making, focusing on chat bots, NLP and NLU, intents, sentiment analysis, data needs, and dynamic AI risks.

Establish a business hypothesis to define value and how to achieve it. Express it as X leads to Y, for example improving response rate by 1% yields 100 customers.

Lock down the model's population and target to align with the business process. Scrub data before modeling and use the expected value, likelihood times value, to rank prospects.

Ensure data is available going forward and consistent, with stable features and pipelines ready for production, and test readiness with a one-time data pull.

Measure the utility of a regression model from a business perspective using simulation. Establish baselines, plan data splits, and consider seasonality and time window effects to evaluate business process performance.

Explore how to measure regression performance with mean absolute error (MAE). Compute absolute errors from model predictions and interpret MAE in dollars for business insights.

Evaluate a regression-based method for ranking prospects by simulating top-100 calls, compare mean absolute error to the baseline, and estimate ROI from historical outcomes and probabilistic results.

Apply faithful simulations of repeated business processes, such as outbound marketing, accounting for non-independent events and changing customer populations, and run many simulations to produce robust, aggregated estimates over time.

Establish a baseline, use simulation, and analyze a binary classifier through the confusion matrix, accounting for type one and type two errors, prediction probabilities, and accuracy weaknesses.

Examine accuracy limitations in classification, reveal how class imbalance and majority class bias can inflate metrics, and suggest area under the curve as a more reliable measure of predictive power.

Compare two cancer classifiers via confusion matrices, focusing on outcome value over accuracy. Prioritize classifier two for its 90% sensitivity, accepting some false positives to minimize missed cancers.

Learn to validate a binary classifier with a calibration plot of predicted probabilities versus actual outcomes, using deciles to assess calibration and model fidelity.

Evaluate the business viability of machine learning initiatives by assessing pre and post modeling feasibility, ROI, data readiness, and costs, then use simulation to inform go/no-go decisions and model maintenance.

Assess your organization's context for machine learning to decide whether to build or outsource. Explore how mlops, talent decisions, data considerations, and cloud implications shape ml capability.

Evaluate business software with AI or ML by scrutinizing learning process, model inputs, and updates; ask about outcomes, training data, model specificity, and data flow to separate hype from value.

Explore deployment configurations for machine learning systems across desktop software, enterprise solutions, and third-party hosted models, and assess data flows, telemetry, and risk.

Evaluate machine learning software by asking about transparency, data sources, and where learning occurs; assess dependencies, customization, governance, risk, and ROI to avoid inefficiencies.

Learn how to plan, manage, and govern ML consulting engagements with emphasis on data readiness, information privacy and security, specialization, integration, validation, maintenance, and documentation.

Build robust data infrastructure by preserving timestamped data with traceability, linking across sources at the lowest level, and preserving ground truth for predictors and outcomes while handling missing data.

Understand the two compute stages for machine learning—development and inference—and how memory and gpus or tpus drive performance, enable api hosting, containerization, load balancing, and scalable deployment.

Explore opportunities for today and the future by applying machine learning, running use case workshops, and utilizing probability and multiple models to guide decisions.

Explore AutoML as automation across the machine learning process from development to deployment, while acknowledging that model developers and data scientists remain essential for feature engineering, interpretability, and explainability.