{"id": "urn:uuid:fe783879-c147-4556-a45e-ec3e7a4bf828", "type": ["VerifiableCredential", "OpenBadgeCredential"], "proof": {"type": "Ed25519Signature2020", "created": "2026-06-18T23:28:43Z", "proofValue": "z4oDAduGstkuTN8QY7jrZqWtMkwJHoBA976P31UNM2Wr3kXi4ihY4zLSQo9urEixPqxuN1tx8RG1HYzUiod3jA5Yk", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow#z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow"}, "issuer": {"id": "did:key:z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow", "name": "MIT Learn", "type": ["Profile"], "image": {"id": "https://learn.mit.edu/images/mit-red.png", "type": "Image", "caption": "MIT Learn logo"}}, "@context": ["https://www.w3.org/ns/credentials/v2", "https://purl.imsglobal.org/spec/ob/v3p0/context-3.0.3.json", "https://w3id.org/security/suites/ed25519-2020/v1"], "validFrom": "2026-06-18T23:28:43Z", "credentialSubject": {"type": ["AchievementSubject"], "identifier": [{"salt": "not-used", "type": "IdentityObject", "hashed": false, "identityHash": "Joshua Eneojo Ayegba ", "identityType": "name"}], "achievement": {"id": "https://learn.mit.edu/courses/course-v1:UAI_SOURCE+UAI.4", "name": "Supervised and Unsupervised Learning", "type": ["Achievement"], "criteria": {"narrative": "- Understand\u00a0the role of data and analytics in decision-making: Learn how data-driven approaches can improve decision-making in various domains, from sports to healthcare.\r\n- Apply\u00a0statistical and machine learning models: Gain hands-on experience with models like linear regression, logistic regression, and decision trees.\r\n- Interpret\u00a0model results: Learn how to interpret coefficients, odds ratios, and tree splits to make informed decisions.\r\n- Evaluate\u00a0model performance: Understand how to use metrics like\u00a0R\u00b2,\u00a0AUC,\u00a0and\u00a0accuracy\u00a0to assess model performance.\r\n- Compare\u00a0models to traditional approaches: Learn how data-driven models can complement or outperform traditional methods, such as expert opinions or subjective assessments.\u00a0\r\n- Define and apply clustering methods: Understand the role of clustering in unsupervised learning, and learn how k-means and hierarchical clustering group data points into meaningful segments.\r\n- Prepare and normalize data for clustering: Recognize why scaling and normalization are essential for fair comparisons across features with different units.\r\n- Determine the number of clusters: Use tools such as scree plots to identify the \u201csweet spot\u201d for distinct and actionable groupings.\r\n- Interpret and communicate clusters: Analyze centroids, dendrograms, and segmentation outputs to describe patterns in the data.\r\n- Make clusters interpretable: Combine clustering with Optimal Classification Trees (OCT) to generate transparent, rule-based profiles that decision-makers can easily understand and apply."}, "description": "Joshua Eneojo Ayegba  has successfully completed all modules and earned a Course Certificate in Supervised and Unsupervised Learning.", "achievementType": "Course"}, "activityEndDate": "2026-06-18T23:28:43Z", "activityStartDate": "2026-06-06T10:40:07Z"}}