Frame real-world problems as machine learning tasks.
School of Data & AIData & AIIntermediateIncluded in a Professional Diploma
Applied Machine Learning
Turn machine learning concepts into practical models and real project work.
Learn how to prepare data, engineer features, train models, evaluate performance, improve results, interpret outputs, and present machine learning projects clearly.
Duration
8 weeks - 6-8 hours/week
Project
Frame real-world problems as machine learning tasks.
Support
Pricing and enrolment are handled through the Professional Diploma
Overview
A practical Short Course built around a visible project.
Apply machine learning to realistic datasets through feature engineering, model selection, evaluation, tuning, interpretation, and project presentation.
Prepare datasets for modeling.
Create and select useful features.
Train and compare multiple machine learning models.
Evaluate models using appropriate metrics.
Improve models through tuning and iteration.
Interpret model results and explain limitations.
Avoid common modeling mistakes and data leakage.
Communicate machine learning results clearly.
Build portfolio-ready applied ML projects.
Course roadmap
What you will work through.
The sequence below is specific to this course. It shows the phases, modules, lessons, and page outlines that move you toward Frame real-world problems as machine learning tasks..
1Phase 1 - Machine Learning ThinkingBuild applied ML judgment before coding: what ML is, how it works, problem types, when not to use ML, and problem framing.2 modules9 lessons1–2 weeks
Module 1: Introduction to Applied Machine LearningUnderstand machine learning as a practical pattern-learning workflow, not just scikit-learn model running.4 lessons
Lesson 1: What Is Machine Learning?Understand machine learning as a pattern-learning approach for prediction, classification, segmentation, and decision support.85 minarticle6 pages
Welcome and Learning Objectives
Introduce applied machine learning and its real purpose.
8 min
Machine Learning in Plain English
Explain ML without unnecessary theory.
18 min
ML vs Rules, Analytics and AI
Clarify common confusion.
22 min
Prediction vs Explanation
Teach the difference between predicting and explaining.
18 min
Model as Pattern Learner
Explain the idea of models learning from features and labels.
18 min
Exercise - Scenario Classification
Students classify business scenarios.
19 min
Lesson 2: How Machine Learning WorksUnderstand features, labels, models, training, prediction, evaluation, and generalization in the ML workflow.85 minarticle5 pages
Welcome and Learning Objectives
Introduce the ML workflow.
8 min
The Core ML Workflow
Explain the complete workflow.
22 min
Features, Labels and Predictions
Explain model inputs and outputs.
20 min
Generalization and Evaluation
Explain why testing on unseen data matters.
18 min
Exercise - ML Workflow Mapping
Students map ML workflows across use cases.
25 min
Lesson 3: Types of Machine LearningUnderstand supervised learning, unsupervised learning, regression, classification, clustering, forecasting, recommendation systems, and reinforcement learning at a high level.80 minarticle4 pages
Welcome and Learning Objectives
Introduce ML types.
8 min
Supervised vs Unsupervised Learning
Explain major categories.
20 min
Common ML Problem Types
Explain problem types.
22 min
Exercise - Problem Type Classifier
Students identify ML problem types.
30 min
Lesson 4: When Not to Use Machine LearningLearn when ML is inappropriate because rules, analytics, poor data, low value, cost, risk, or explainability constraints make a simpler approach better.80 minarticle4 pages
Welcome and Learning Objectives
Introduce ML appropriateness.
8 min
When Simpler Is Better
Explain alternatives to ML.
22 min
High-Risk ML Situations
Explain risks.
20 min
Exercise - ML Appropriateness Review
Students decide whether ML is appropriate.
30 min
Module 2: ML Problem FramingConvert business problems into clear ML problem statements with targets, features, timing, metrics, and risk notes.5 lessons
Lesson 1: Business Problem to ML ProblemConvert business objectives into ML objectives, target variables, prediction windows, data availability checks, and success metrics.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Defining TargetsDefine target variables, labels, positive/negative classes, regression targets, and leakage risk in targets.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Defining FeaturesDefine safe input features including numerical, categorical, date/time, text, historical, and leakage-prone features.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 4: ML Success MetricsChoose model and business metrics based on cost of wrong predictions, usefulness, and stakeholder expectations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 5: Mini Project 1 - ML Problem Framing BriefProduce a professional ML problem framing brief for a selected business scenario.100 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
2Phase 2 - ML Data Preparation and Leakage PreventionPrepare ML datasets safely with quality review, splitting, leakage prevention, missing values, encoding, scaling, and scikit-learn workflows.2 modules11 lessons2 weeks
Module 1: Preparing ML DatasetsPrepare raw business data for modeling without leakage.6 lessons
Lesson 1: Data Quality Review for MLAudit missing values, duplicates, invalid categories, outliers, date issues, inconsistent IDs, and target quality.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Train Validation and Test SplitsUnderstand training, validation, test, random, stratified, and time-based splits.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Data LeakageIdentify future information, target-derived fields, post-outcome features, duplicate leakage, and aggregation leakage.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 4: Missing Value HandlingHandle missingness with dropping, imputation, missing indicators, business meaning, and pipeline-safe imputation.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Categorical EncodingUse one-hot, ordinal, frequency, high-cardinality, rare category handling, and avoid encoding mistakes.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 6: Scaling Numerical FeaturesApply standardization, normalization, and understand when scaling matters.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Module 2: Scikit-Learn Workflow FoundationsBuild first models, preprocessing pipelines, reproducible notebooks, and baseline dataset packages.5 lessons
Lesson 1: First Scikit-Learn ModelUse estimators, fit, predict, train/test workflow, model objects, and prediction outputs.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 2: Preprocessing PipelinesUse pipelines, ColumnTransformer, numerical preprocessing, categorical preprocessing, and leakage prevention.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 3: ReproducibilityUse random_state, deterministic splits, environment setup, notebook hygiene, and experiment notes.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Baseline Dataset PackagePackage clean dataset, feature list, target definition, split strategy, and preprocessing plan.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Milestone Project 1 - ML-Ready Dataset PackagePrepare a messy dataset into a modeling-ready package.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
3Phase 3 - Baselines, Regression and ClassificationBuild baselines, regression models, and classification models with honest evaluation and business interpretation.3 modules15 lessons2 weeks
Module 1: Baseline ModelsBuild and explain simple baselines before training more complex models.3 lessons
Lesson 1: Why Baselines MatterUse simple, mean, median, majority class, and business-rule baselines.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Baseline MetricsEvaluate regression baseline error, classification accuracy, majority class trap, business comparison, and useful-enough threshold.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 3: Baseline CommunicationExplain baselines to stakeholders and avoid fake performance claims.50 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
25 min
Practice Activity
Apply the lesson through a guided exercise.
17 min
Module 2: Regression ModelsTrain, evaluate, improve, and explain regression models.6 lessons
Lesson 1: Regression Problem FramingFrame continuous-value prediction problems such as sales, price, revenue, and demand.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Linear RegressionTrain and interpret linear regression with coefficients, intercepts, residuals, and assumptions.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Multiple Linear RegressionBuild multi-feature regression and interpret feature impact and multicollinearity intuition.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Regression MetricsUse MAE, MSE, RMSE, R², MAPE where useful, and business interpretation of error.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Regression ImprovementImprove regression using feature selection, transformations, outlier handling, cross-validation preview, and model comparison.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 6: Milestone Project 2 - Sales Price or Demand Prediction ModelBuild and evaluate a regression project.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Module 3: Classification ModelsTrain and evaluate classification models for churn, risk, fraud, leads, and completion risk.6 lessons
Lesson 1: Classification Problem FramingFrame category prediction problems such as churn, fraud, loan approval, lead scoring, and completion risk.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Logistic RegressionTrain and interpret logistic regression with probabilities, thresholds, and coefficients.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Classification MetricsUse accuracy, precision, recall, F1, confusion matrix, ROC-AUC intuition, and cost of errors.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 4: Decision TreesUse decision tree splits, rules, interpretability, overfitting, and tree depth.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Random ForestsUse ensembles, bagging, stability, feature importance, and tradeoffs.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 6: Milestone Project 3 - Churn Risk or Lead Classification ModelBuild and compare classification models with business interpretation.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
4Phase 4 - Model Evaluation, Validation and TuningEvaluate, validate, tune, and audit ML models using appropriate metrics and error analysis.2 modules10 lessons1–2 weeks
Module 1: Evaluating Models ProperlyUnderstand overfitting, cross-validation, validation strategy, metrics, and model QA.5 lessons
Lesson 1: Overfitting and UnderfittingUnderstand training/validation/test performance, bias, variance, and model complexity.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Cross-ValidationUse k-fold, stratified folds, cross-validation scores, and metric variability.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Validation StrategyChoose train/validation/test, cross-validation, time-based validation, group-based split, and avoid test misuse.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 4: Choosing the Right MetricChoose metrics based on business error cost and model purpose.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Model Validation ChecklistCheck leakage, splits, metrics, baselines, error analysis, and stakeholder interpretation.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Module 2: Hyperparameter TuningTune models carefully and analyze errors after tuning.5 lessons
Lesson 1: What Hyperparameters AreDifferentiate parameters and hyperparameters, model complexity, tuning options, and blind tuning risks.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Grid Search and Random SearchUse search spaces, cross-validation, scoring, computational cost, and practical tuning workflow.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Avoiding Tuning MistakesAvoid test set misuse, validation overfitting, too many experiments, wrong metrics, and ignoring business cost.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Error Analysis After TuningReview segment-level errors, false positives, false negatives, residuals, and business cost.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Mini Project 2 - Model Evaluation AuditAudit model outputs and recommend whether the model is usable.90 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
5Phase 5 - Feature Engineering and Advanced Supervised LearningEngineer useful features and compare stronger supervised model families.2 modules10 lessons1–2 weeks
Module 1: Feature Engineering for Tabular DataCreate leakage-safe numerical, categorical, date/time, and aggregation features.5 lessons
Lesson 1: What Makes a Good FeatureEvaluate predictive signal, business meaning, leakage risk, stability, usefulness, and feature cost.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Numerical Feature EngineeringCreate ratios, differences, bins, log transforms, outlier handling, and interaction features.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Categorical Feature EngineeringEngineer categorical features with one-hot, ordinal, frequency, rare category handling, and high-cardinality strategies.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 4: Date and Time FeaturesCreate day, week, month, recency, tenure, seasonality, and time-since-last-event features.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Aggregation FeaturesCreate customer-level, product-level, rolling, group-based, and leakage-safe aggregation features.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Module 2: Stronger Model FamiliesCompare regularized linear models, tree-based models, gradient boosting, and model selection strategies.5 lessons
Lesson 1: Regularized Linear ModelsUse Ridge, Lasso, Elastic Net, simplicity vs performance, and when linear models win.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 2: Tree-Based Models RevisitedCompare tree depth, random forests, Extra Trees, feature importance, and stability.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Gradient BoostingUnderstand boosting, XGBoost/LightGBM/CatBoost concepts, performance, overfitting risk, and use cases.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Model Selection StrategyBalance accuracy, interpretability, speed, performance, data size, maintenance, and business constraints.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 5: Milestone Project 4 - Feature Engineering and Model Comparison ProjectEngineer features and compare candidate model families.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
6Phase 6 - Imbalanced Data and Real-World ClassificationHandle rare-event classification and design real-world decision workflows.2 modules9 lessons1–2 weeks
Module 1: Imbalanced ClassificationDeal with fraud, churn, medical risk, rare events, metrics, resampling, class weights, and thresholds.4 lessons
Lesson 1: Why Imbalanced Data Is HardUnderstand fraud, churn, medical risk, rare events, and accuracy traps.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Metrics for Imbalanced ProblemsUse precision, recall, F1, ROC-AUC, PR-AUC, confusion matrix, and cost matrix.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Resampling and Class WeightsCompare oversampling, undersampling, SMOTE concept, class weights, and risks.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Threshold TuningChoose decision thresholds based on precision/recall tradeoff and business cost.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Module 2: Real-World Classification WorkflowsDesign churn, fraud/risk, lead scoring, and education risk workflows responsibly.5 lessons
Lesson 1: Churn Prediction WorkflowDefine churn, observation window, prediction window, retention action, and evaluation.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 2: Fraud or Risk Detection WorkflowDesign rare event workflows with false positives, false negatives, human review, and escalation.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Lead Scoring WorkflowPredict conversion, prioritize leads, rank leads, and map sales action.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Education Risk Prediction WorkflowPredict completion risk with student support, intervention planning, and ethical concerns.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Milestone Project 5 - Fraud Churn or Risk ModelBuild an imbalanced classification model with decision threshold and memo.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
7Phase 7 - Unsupervised Learning and Customer SegmentationUse clustering and segmentation for pattern discovery and business action.2 modules9 lessons1 week
Module 1: Clustering and Pattern DiscoveryLearn unsupervised learning, K-means, cluster evaluation, and dimensionality reduction intuition.4 lessons
Lesson 1: What Is Unsupervised LearningUnderstand no-label pattern discovery, segmentation, exploration, and limitations.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: K-Means ClusteringUse clusters, centroids, distance, number of clusters, and scaling importance.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Evaluating ClustersUse inertia, elbow method, silhouette intuition, usefulness, and segment interpretation.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 4: Dimensionality Reduction IntroUnderstand PCA intuition, visualization, high-dimensional data, limitations, and when not to overuse PCA.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Module 2: Segmentation for Business ActionProfile segments, validate usefulness, and turn segments into strategy.5 lessons
Lesson 1: Customer Segmentation StrategyDesign behavior, value, engagement, lifecycle, and actionable segments.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Segment ProfilingProfile segments by size, revenue behavior, engagement, risk, preferences, and actions.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Segment ValidationAssess stability, business meaning, stakeholder review, actionability, and fake clusters.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: From Segments to StrategyTurn clusters into targeting, retention, recommendations, product improvements, and support prioritization.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 5: Milestone Project 6 - Customer Segmentation ProjectBuild a customer segmentation project with business recommendations.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
8Phase 8 - Forecasting, Text ML and RecommendationsBuild applied prototypes for forecasting, text classification, and recommender systems.3 modules15 lessons2 weeks
Module 1: Forecasting and Time-Series MLBuild time-aware forecasting datasets, baselines, models, and evaluation.5 lessons
Lesson 1: Forecasting vs RegressionUnderstand time order, trend, seasonality, lag features, future leakage, and backtesting.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Time-Series Feature EngineeringCreate lag features, rolling averages, expanding windows, calendar features, and event features.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Forecasting BaselinesBuild naive, moving average, seasonal naive, and explain why baselines matter.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: ML for ForecastingFrame forecasting as supervised learning with time-based splits and regression models.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 5: Forecast EvaluationUse MAE, RMSE, MAPE, forecast bias, backtesting, and business usefulness.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Module 2: Text ML and NLP BasicsBuild simple text cleaning, feature extraction, classification, and evaluation workflows.4 lessons
Lesson 1: Working with Text DataClean text with tokenization concept, stop words, n-grams, and label quality.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Text Feature ExtractionUse bag of words, TF-IDF, sparse features, limitations, and feature size.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Text ClassificationBuild sentiment, ticket, spam, or review categorization models.75 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
37 min
Practice Activity
Apply the lesson through a guided exercise.
30 min
Lesson 4: Evaluating Text ModelsUse precision/recall, confusion matrix, misclassified examples, ambiguous labels, and human review.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Module 3: Recommendation Systems FoundationsBuild popularity-based and content-based recommender prototypes and understand collaborative filtering concepts.6 lessons
Lesson 1: Why Recommendations MatterUnderstand product, content, course recommendations, personalization, and business value.55 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
27 min
Practice Activity
Apply the lesson through a guided exercise.
20 min
Lesson 2: Popularity-Based RecommendersBuild most popular, trending, top-rated, segment-based, and baseline recommenders.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 3: Content-Based RecommendationsUse item features, similarity, user preferences, matching logic, and cold start.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Collaborative Filtering ConceptsExplain user-item interactions, similar users/items, matrix intuition, cold start, and limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 5: Evaluating RecommendationsPlan relevance, diversity, coverage, click-through, conversion, feedback loops, and offline vs online evaluation.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 6: Mini Project 3 - Applied ML Specialization SprintComplete one specialization sprint: forecasting, text classification, or recommendation prototype.120 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
9Phase 9 - Interpretation, Responsible ML and Deployment HandoffInterpret models, package ML projects, write handoff documents, and review responsible ML concerns.3 modules13 lessons1–2 weeks
Module 1: Model Interpretation and ExplainabilityExplain model behavior, errors, risk, and stakeholder-ready results.4 lessons
Lesson 1: Feature ImportanceInterpret tree importance, coefficients, permutation importance, and limitations.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Error AnalysisAnalyze bad performance, segment errors, outlier errors, false positives/negatives, and data gaps.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 3: Explainability BasicsUnderstand interpretability vs accuracy, SHAP/LIME high-level, local/global explanations, and when explainability matters.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Communicating ML ResultsWrite business framing, performance summary, risks, limitations, recommended use, and what not to claim.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Module 2: ML Packaging and Deployment HandoffPackage models, artifacts, batch scoring, handoff documents, and monitoring notes.4 lessons
Lesson 1: Professional ML Project StructureStructure notebooks, src, data, models, reports, requirements, README, and reproducibility.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 2: Saving Models and ArtifactsSave models, encoders, scalers, feature lists, versions, and reproducibility artifacts.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 3: Batch Prediction WorkflowCreate batch scoring, input validation, output files, prediction reports, and monitoring concepts.70 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
35 min
Practice Activity
Apply the lesson through a guided exercise.
27 min
Lesson 4: Deployment HandoffDocument input/output contract, feature requirements, model limitations, monitoring notes, and retraining notes.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Module 3: Responsible Applied MLReview fairness, privacy, monitoring, and model cards.5 lessons
Lesson 1: Bias and Fairness ReviewAudit sensitive features, proxy variables, segment performance, fairness risks, and human oversight.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 2: Privacy and Data GovernanceReview personal data, consent, minimization, secure storage, and responsible prediction sharing.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 3: Model Monitoring BasicsDesign monitoring for data drift, concept drift, performance decay, monitoring metrics, and retraining triggers.60 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
30 min
Practice Activity
Apply the lesson through a guided exercise.
22 min
Lesson 4: Model CardsCreate model cards with intended use, training data, metrics, limitations, ethics, and monitoring recommendations.65 minarticle3 pages
Overview and Learning Objectives
Introduce the lesson and clarify expected outcomes.
8 min
Concepts and Practical Examples
Explain the concept with practical ML examples.
32 min
Practice Activity
Apply the lesson through a guided exercise.
25 min
Lesson 5: Milestone Project 7 - Model Explanation and Handoff PackagePackage model explanation, responsible-use review, handoff, and monitoring assets.130 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
10Phase 10 - CapstoneComplete an end-to-end applied machine learning capstone with professional packaging and presentation.1 modules3 lessons1–2 weeks
Module 1: Applied Machine Learning CapstoneStudents choose one serious ML project and complete it end-to-end.3 lessons
Lesson 1: Capstone OptionsChoose a serious applied machine learning capstone option.60 minarticle1 pages
Choose Your Applied ML Capstone
Review approved capstone options.
60 min
Lesson 2: Final Capstone - Applied Machine Learning CapstoneBuild a complete applied machine learning project from brief to model package and executive presentation.220 minarticle2 pages
Project Brief
Explain the project scenario and expected output.
20 min
Review Checklist
Checklist for project quality.
20 min
Lesson 3: Graduation Requirements and Portfolio OutcomeClarify completion requirements and expected portfolio outputs.55 minarticle1 pages
Requirements and Portfolio Checklist
Summarize graduation requirements and portfolio assets.
55 min
Tools and skills
Build skill with the tools used in the work.
Frame real-world problems as machine learning tasks.Prepare datasets for modeling.Create and select useful features.Train and compare multiple machine learning models.Evaluate models using appropriate metrics.Improve models through tuning and iteration.Interpret model results and explain limitations.Avoid common modeling mistakes and data leakage.Communicate machine learning results clearly.Build portfolio-ready applied ML projects.
Projects and exercises
- Frame real-world problems as machine learning tasks.
- Structured exercises
- Portfolio practice
Resources included
- Course resources
- Project guidance
- Learners building practical tech skills
- A willingness to practice consistently
Career relevance
Applied Machine Learning supports practical career readiness.
Related Professional Diploma
View Professional DiplomaData Science & Machine Learning
Build practical data science and machine learning skills by learning Python, statistics, data preparation, model training, evaluation, interpretation, and end-to-end data science projects.
FAQ
Questions about this Short Course.
Short Course answers about scope, projects, support, and next steps.
Machine Learning Foundations teaches the core concepts. Applied Machine Learning focuses on using those concepts to build practical projects with realistic datasets.
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