[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"sanity-ISu3F0JdymSCyhgEF6kyncaZjpZOqjKQYBhofKgyH84":3,"sanity-V0yd5ftuXoi4JGpHomGnN26eCTUGe60ngzQUd5OhQnw":236},{"data":4,"sourceMap":-1},{"latestPodcast":5,"latestReleases":14,"post":39,"recent":211},[6],{"_id":7,"publishedAt":8,"slug":9,"sponsored":12,"title":13},"d53f9358-3bb2-4f69-aebe-d31d19522cd4","2026-07-10T07:40:00.000Z",{"_type":10,"current":11},"slug","building-more-than-just-an-agent-harness",null,"Building more than just an agent harness",[15,21,27,33],{"_id":16,"publishedAt":17,"slug":18,"title":20},"eb5b66eb-9410-4329-83bb-22bbff39402a","2026-04-28T13:00:00.000Z",{"_type":10,"current":19},"turn-scattered-knowledge-into-trusted-intelligence","Turning scattered knowledge into trusted intelligence: Stack Internal 2026.3",{"_id":22,"publishedAt":23,"slug":24,"title":26},"369c2401-b62e-4a37-8ff8-bf603023ecad","2026-03-02T15:03:00.988Z",{"_type":10,"current":25},"what-s-new-at-stack-overflow-march-2026","What’s new at Stack Overflow: March 2026",{"_id":28,"publishedAt":29,"slug":30,"title":32},"5e9053a4-07ea-447c-91ea-29e0b6228537","2026-02-02T15:00:00.000Z",{"_type":10,"current":31},"what-s-new-at-stack-overflow-february-2026","What’s new at Stack Overflow: February 2026",{"_id":34,"publishedAt":35,"slug":36,"title":38},"a1b538eb-a8a6-46d0-80a1-ac70ec9bb935","2026-01-05T10:00:00.000-05:00",{"_type":10,"current":37},"what-s-new-at-stack-overflow-january-2026","What’s new at Stack Overflow: January 2026",{"_createdAt":40,"_id":41,"_rev":42,"_type":43,"_updatedAt":44,"author":45,"body":62,"comments":171,"dateUrl":172,"excerpt":173,"image":174,"legacyBody":177,"product":12,"publishedAt":180,"slug":181,"sponsored":12,"tags":183,"title":210,"visible":171},"2023-05-25T09:39:18Z","wp-post-18294","dgl3SCUzppW3U2LvCoSupg","blogPost","2023-07-13T14:55:59Z",[46],{"_createdAt":47,"_id":48,"_rev":49,"_type":50,"_updatedAt":51,"avatar":52,"bio":57,"employee":58,"name":59,"slug":60},"2023-05-23T16:27:18Z","wp-author-cap-17508","07ZbrKPSUrjrV4wQ6fDpaa","blogAuthor","2023-06-20T15:05:10Z",{"_type":53,"asset":54},"image",{"_ref":55,"_type":56},"image-8c28cb2ef9d5c7ef909c7685c3808e5c66f17aeb-400x400-jpg","reference","Curriculum Developer, Codecademy","none","Sophie Sommer",{"current":61},"sophie-sommer",[63,74,78,86,100,111,152],{"_key":64,"_type":65,"children":66,"markDefs":72,"style":73},"369d3083edf0","block",[67],{"_key":68,"_type":69,"marks":70,"text":71},"369d3083edf00","span",[],"In the fourth lesson of the series, we'll talk about the matrix representation of the linear regression problem. In the process, we'll discuss the basics of matrix multiplication. We'll also see how this mathematical understanding can prepare us to make sense of error messages that we might encounter when fitting a model in Python.",[],"normal",{"_key":75,"_type":76,"markDefs":12,"url":77},"5e8dd33ad073","embed","https:\u002F\u002Fwww.youtube.com\u002Fembed\u002F78c5ohzcWYQ?start=110",{"_key":79,"_type":65,"children":80,"markDefs":85,"style":73},"e84432d52c5c",[81],{"_key":82,"_type":69,"marks":83,"text":84},"e84432d52c5c0",[],"Here are some Stack Overflow questions related to the work we did in today's session:",[],{"_key":87,"_type":65,"children":88,"level":94,"listItem":95,"markDefs":96,"style":73},"93ff603e5fdf",[89],{"_key":90,"_type":69,"marks":91,"text":93},"93ff603e5fdf0",[92],"4bd202a07600","Capturing high multicollinearity in statsmodels",1,"bullet",[97],{"_key":92,"_type":98,"href":99,"reference":12},"link","https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F25676145\u002Fcapturing-high-multi-collinearity-in-statsmodels",{"_key":101,"_type":65,"children":102,"level":94,"listItem":95,"markDefs":108,"style":73},"5c7433789143",[103],{"_key":104,"_type":69,"marks":105,"text":107},"5c74337891430",[106],"d3fabd163519","Logistic Regression in statsmodels “LinAlgError: Singular matrix”",[109],{"_key":106,"_type":98,"href":110,"reference":12},"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F49954406\u002Flogistic-regression-in-statsmodels-linalgerror-singular-matrix",{"_key":112,"_type":65,"children":113,"markDefs":145,"style":73},"396135408da8",[114,118,123,127,132,136,141],{"_key":115,"_type":69,"marks":116,"text":117},"396135408da80",[],"If you want to ask any questions or provide feedback on the lesson, you are welcome to leave a comment on the ",{"_key":119,"_type":69,"marks":120,"text":122},"396135408da81",[121],"3ace7b97a917","YouTube recording of this lesson",{"_key":124,"_type":69,"marks":125,"text":126},"396135408da82",[],". If you’d like to watch a session live, follow the ",{"_key":128,"_type":69,"marks":129,"text":131},"396135408da83",[130],"992b6627aee7","Codecademy YouTube channel",{"_key":133,"_type":69,"marks":134,"text":135},"396135408da84",[],". We'll be live again on Tuesday, June 15 at 11am EDT to discuss polynomial and interaction terms, which can be used to build more flexible regression models. You can join that session ",{"_key":137,"_type":69,"marks":138,"text":140},"396135408da85",[139],"de9961df7964","here",{"_key":142,"_type":69,"marks":143,"text":144},"396135408da86",[],".",[146,148,150],{"_key":121,"_type":98,"href":147,"reference":12},"https:\u002F\u002Fyoutu.be\u002F78c5ohzcWYQ?t=110",{"_key":130,"_type":98,"href":149,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fcodecademy\u002Ffeatured",{"_key":139,"_type":98,"href":151,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IJ-5RZ3GBUY",{"_key":153,"_type":65,"children":154,"markDefs":168,"style":73},"b38162c8ed92",[155,159,164],{"_key":156,"_type":69,"marks":157,"text":158},"b38162c8ed920",[],"Finally, if you want even more linear regression content, you can sign up for the ",{"_key":160,"_type":69,"marks":161,"text":163},"b38162c8ed921",[162],"bcbfcb3762fe","Linear Regression in Python interactive course",{"_key":165,"_type":69,"marks":166,"text":167},"b38162c8ed922",[]," this series was based on. This course was developed by Sophie and has many more quizzes, projects, and helpful nuggets that we can’t fit into our streams!",[169],{"_key":162,"_type":98,"href":170,"reference":12},"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Flinear-regression-mssp?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1",true,"2021\u002F06\u002F12","",{"_type":53,"asset":175},{"_ref":176,"_type":56},"image-f5b272e299c874f83358613fe0855ad7f7ea164c-2400x1240-png",{"code":178,"language":179},"\u003C!-- wp:paragraph -->\n\u003Cp>In the fourth lesson of the series, we'll talk about the matrix representation of the linear regression problem. In the process, we'll discuss the basics of matrix multiplication. We'll also see how this mathematical understanding can prepare us to make sense of error messages that we might encounter when fitting a model in Python.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:html -->\n\u003Ciframe width=\"560\" height=\"560\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002F78c5ohzcWYQ?start=110\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen>\u003C\u002Fiframe>\n\u003C!-- \u002Fwp:html -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Here are some Stack Overflow questions related to the work we did in today's session:\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:jetpack\u002Fmarkdown {\"source\":\"- [Capturing high multicollinearity in statsmodels](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F25676145\u002Fcapturing-high-multi-collinearity-in-statsmodels)\"} -->\n\u003Cdiv class=\"wp-block-jetpack-markdown\">\u003Cul>\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F25676145\u002Fcapturing-high-multi-collinearity-in-statsmodels\">Capturing high multicollinearity in statsmodels\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fdiv>\n\u003C!-- \u002Fwp:jetpack\u002Fmarkdown -->\n\n\u003C!-- wp:jetpack\u002Fmarkdown {\"source\":\"- [Logistic Regression in statsmodels “LinAlgError: Singular matrix”](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F49954406\u002Flogistic-regression-in-statsmodels-linalgerror-singular-matrix)\"} -->\n\u003Cdiv class=\"wp-block-jetpack-markdown\">\u003Cul>\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F49954406\u002Flogistic-regression-in-statsmodels-linalgerror-singular-matrix\">Logistic Regression in statsmodels “LinAlgError: Singular matrix”\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fdiv>\n\u003C!-- \u002Fwp:jetpack\u002Fmarkdown -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>If you want to ask any questions or provide feedback on the lesson, you are welcome to leave a comment on the \u003Ca href=\"https:\u002F\u002Fyoutu.be\u002F78c5ohzcWYQ?t=110\">YouTube recording of this lesson\u003C\u002Fa>. If you’d like to watch a session live, follow the \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fcodecademy\u002Ffeatured\">Codecademy YouTube channel\u003C\u002Fa>. We'll be live again on Tuesday, June 15 at 11am EDT to discuss polynomial and interaction terms, which can be used to build more flexible regression models. You can join that session \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IJ-5RZ3GBUY\">here\u003C\u002Fa>.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Finally, if you want even more linear regression content, you can sign up for the \u003Ca href=\"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Flinear-regression-mssp?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1\">Linear Regression in Python interactive course\u003C\u002Fa> this series was based on. 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How can engineering leaders avoid becoming Bond villains?",{"_id":225,"publishedAt":226,"slug":227,"sponsored":12,"title":229},"4b22c2a3-3779-4966-93eb-5230391dbdce","2026-06-23T14:08:58.595Z",{"_type":10,"current":228},"your-ai-shipped-a-backend-that-boots-that-is-the-whole-problem","Your AI shipped a backend that boots. That is the whole problem.",{"_id":231,"publishedAt":232,"slug":233,"sponsored":12,"title":235},"5cf362e1-fe7b-45af-b69c-914731c6a052","2026-06-23T14:00:00.000Z",{"_type":10,"current":234},"the-2026-developer-survey-is-now-open-for-human-developers-only","The 2026 Developer Survey is now open (for human developers only)!",{"data":237,"sourceMap":-1},{"count":238,"lastTimestamp":12},0]