From 990d3c469d72037fb3065c3014f4f04c0da9a440 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 17 Feb 2026 08:07:35 +0000 Subject: [PATCH] Update notebook outputs to show cleaned Wikipedia content without boilerplate Co-authored-by: leestott <2511341+leestott@users.noreply.github.com> --- 1-Introduction/01-defining-data-science/notebook.ipynb | 2 +- 1-Introduction/01-defining-data-science/solution/notebook.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/1-Introduction/01-defining-data-science/notebook.ipynb b/1-Introduction/01-defining-data-science/notebook.ipynb index 35a1af9b..0e9199fc 100644 --- a/1-Introduction/01-defining-data-science/notebook.ipynb +++ b/1-Introduction/01-defining-data-science/notebook.ipynb @@ -98,7 +98,7 @@ "output_type": "stream", "name": "stdout", "text": [ - " Data science - Wikipedia Data science From Wikipedia, the free encyclopedia Jump to navigation Jump to search Interdisciplinary field of study focused on deriving knowledge and insights from data Not to be confused with information science . The existence of Comet NEOWISE (here depicted as a series of red dots) was discovered by analyzing astronomical survey data acquired by a space telescope , the Wide-field Infrared Survey Explorer . Part of a series on Machine learning and data mining Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning ( classification  • regression ) Decision trees Ensembles Bagging Boosting Random forest k -NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine \n" + "Data science From Wikipedia, the free encyclopedia Interdisciplinary field of study focused on deriving knowledge and insights from data Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain. Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.\n" ] } ], diff --git a/1-Introduction/01-defining-data-science/solution/notebook.ipynb b/1-Introduction/01-defining-data-science/solution/notebook.ipynb index 75e45a91..bd376ae1 100644 --- a/1-Introduction/01-defining-data-science/solution/notebook.ipynb +++ b/1-Introduction/01-defining-data-science/solution/notebook.ipynb @@ -101,7 +101,7 @@ "output_type": "stream", "name": "stdout", "text": [ - " Machine learning - Wikipedia Machine learning From Wikipedia, the free encyclopedia Jump to navigation Jump to search Study of algorithms that improve automatically through experience For the journal, see Machine Learning (journal) . \"Statistical learning\" redirects here. For statistical learning in linguistics, see statistical learning in language acquisition . Part of a series on Artificial intelligence Major goals Artificial general intelligence Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics Approaches Symbolic Deep learning Bayesian networks Evolutionary algorithms Philosophy Ethics Existential risk Turing test Chinese room Control problem Friendly AI History Timeline Progress AI winter Technology Applications Projects Programming languages Glossary Glossary v t e Part of a series on Machine learning and data mining Problems Classification Clustering Regression Anomaly detection Data Cleaning AutoML Associ\n" + "Machine learning From Wikipedia, the free encyclopedia Study of algorithms that improve automatically through experience Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.\n" ] } ],