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Machine_Learning_Assignments_Coursera.html
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url: https://www.coursera.org/learn/machine-learning/home/assignments
saved date: Thu Aug 13 2020 21:58:03 GMT+0700 (Indochina Time)
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) 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) 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class=rc-HeroIcon><div class=rc-DocumentAssignmentStateIcon style=height:110px;width:110px><svg class=_ufjrdd aria-hidden=true focusable=false style=fill:rgb(117,117,117);height:99px;width:99px;position:absolute;top:0px;left:5px viewBox="0 0 96 96" role=img aria-labelledby="DocumentIcon05121c55-fb74-47be-f0e6-d6db04beff08 DocumentIcon05121c55-fb74-47be-f0e6-d6db04beff08Desc" xmlns=http://www.w3.org/2000/svg><g id=New-Icons stroke=none stroke-width=1 fill-rule=evenodd role=presentation><g id=SvgProgressDoc fill-rule=nonzero><path d="M66,3 L15,3 L15,93 L81,93 L81,18 L66,18 L66,3 Z M69,4.5 L69,15 L79.5,15 L69,4.5 Z M12,0 L69,0 L84,15 L84,96 L12,96 L12,0 Z M28.5,32 C27.6715729,32 27,31.3284271 27,30.5 C27,29.6715729 27.6715729,29 28.5,29 C29.3284271,29 30,29.6715729 30,30.5 C30,31.3284271 29.3284271,32 28.5,32 Z M36,29 L68,29 L68,32 L36,32 L36,29 Z M28.5,41 C27.6715729,41 27,40.3284271 27,39.5 C27,38.6715729 27.6715729,38 28.5,38 C29.3284271,38 30,38.6715729 30,39.5 C30,40.3284271 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aria-labelledby="greencheckmarkd6909099-a2ba-4e99-82cc-f08c8e1fbc18 greencheckmarkd6909099-a2ba-4e99-82cc-f08c8e1fbc18Desc" xmlns=http://www.w3.org/2000/svg><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div></div></div><h2 class=hero-unit-text><span>You passed this course! Your grade is 97.60%.</span></h2></div></div></div><h2 class=rc-A11yScreenReaderOnly>Assignments</h2><div class=assignments-page-table role=grid><div class="rc-AssignmentsTableHeader horizontal-box align-items-vertical-center" role=row><div class="item-header flex-1 horizontal-box"><div class=item-header-icon-space aria-hidden=true></div><div class="item-header-text flex-1" role=columnheader>Item</div></div><div class="status-header horizontal-box"><div class=status-header-icon-space aria-hidden=true></div><div class="header-text status-header-text" role=columnheader>Status</div></div><div class="due-header horizontal-box"><div class=due-header-icon-space aria-hidden=true></div><div class="header-text due-header-text" role=columnheader>Due</div></div><div class="weight-header horizontal-box"><div class="header-text weight-header-text" role=columnheader>Weight</div></div><div class="grade-header horizontal-box"><div class="header-text grade-header-text" role=columnheader>Grade</div></div></div><div class=rc-UngroupedAssignmentsTable><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed12929613-699f-4713-f9b7-c77e7bde81ee Completed12929613-699f-4713-f9b7-c77e7bde81eeDesc" xmlns=http://www.w3.org/2000/svg><title id=Completed12929613-699f-4713-f9b7-c77e7bde81ee>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/wjqip/introduction","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/wjqip/introduction href=https://www.coursera.org/learn/machine-learning/exam/wjqip/introduction class=nostyle> <strong> Introduction </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation12720774-f771-48da-d257-3b6ea19b6964 statusexplanation12720774-f771-48da-d257-3b6ea19b6964Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation12720774-f771-48da-d257-3b6ea19b6964>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Apr 20</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>80%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed9e030a11-ffa6-43bd-f1f3-9f43c4b30a80 Completed9e030a11-ffa6-43bd-f1f3-9f43c4b30a80Desc" xmlns=http://www.w3.org/2000/svg><title id=Completed9e030a11-ffa6-43bd-f1f3-9f43c4b30a80>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/QeJ50/linear-regression-with-one-variable","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/QeJ50/linear-regression-with-one-variable href=https://www.coursera.org/learn/machine-learning/exam/QeJ50/linear-regression-with-one-variable class=nostyle> <strong> Linear Regression with One Variable </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanationfafc64d7-4d49-48b5-c421-b462e84c37a2 statusexplanationfafc64d7-4d49-48b5-c421-b462e84c37a2Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanationfafc64d7-4d49-48b5-c421-b462e84c37a2>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Apr 20</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>80%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed0ae3b1bf-b358-4701-afc9-6780aebb230b Completed0ae3b1bf-b358-4701-afc9-6780aebb230bDesc" xmlns=http://www.w3.org/2000/svg><title id=Completed0ae3b1bf-b358-4701-afc9-6780aebb230b>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/7pytE/linear-regression-with-multiple-variables","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/7pytE/linear-regression-with-multiple-variables href=https://www.coursera.org/learn/machine-learning/exam/7pytE/linear-regression-with-multiple-variables class=nostyle> <strong> Linear Regression with Multiple Variables </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation6339d6ed-3266-4f13-a7e2-2fa0fce4548f statusexplanation6339d6ed-3266-4f13-a7e2-2fa0fce4548fDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation6339d6ed-3266-4f13-a7e2-2fa0fce4548f>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Apr 27</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completeda0441384-380e-4e61-ddea-227430b0b08f Completeda0441384-380e-4e61-ddea-227430b0b08fDesc" xmlns=http://www.w3.org/2000/svg><title id=Completeda0441384-380e-4e61-ddea-227430b0b08f>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial href=https://www.coursera.org/learn/machine-learning/exam/dbM1J/octave-matlab-tutorial class=nostyle> <strong> Octave/Matlab Tutorial </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation1aff029e-aa98-443d-a241-2ae09e45b790 statusexplanation1aff029e-aa98-443d-a241-2ae09e45b790Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation1aff029e-aa98-443d-a241-2ae09e45b790>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Apr 27</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>80%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completedfbdd00c2-b12a-42b1-fc6f-197cd3ac2d8d Completedfbdd00c2-b12a-42b1-fc6f-197cd3ac2d8dDesc" xmlns=http://www.w3.org/2000/svg><title id=Completedfbdd00c2-b12a-42b1-fc6f-197cd3ac2d8d>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/programming/8f3qT/linear-regression","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/programming/8f3qT/linear-regression href=https://www.coursera.org/learn/machine-learning/programming/8f3qT/linear-regression class=nostyle> <strong> Linear Regression </strong> </a></div><div class=item-subtitle-text>Programming Assignment</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanationbed3d1a9-ebcc-469f-f252-6cf36f0febea statusexplanationbed3d1a9-ebcc-469f-f252-6cf36f0febeaDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanationbed3d1a9-ebcc-469f-f252-6cf36f0febea>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Apr 27</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>8.33%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completede3f7a13d-5f68-4282-f330-7596ddb6b5d4 Completede3f7a13d-5f68-4282-f330-7596ddb6b5d4Desc" xmlns=http://www.w3.org/2000/svg><title id=Completede3f7a13d-5f68-4282-f330-7596ddb6b5d4>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/Fd9cu/logistic-regression","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/Fd9cu/logistic-regression href=https://www.coursera.org/learn/machine-learning/exam/Fd9cu/logistic-regression class=nostyle> <strong> Logistic Regression </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation8bce7960-4a30-4e42-ed88-30df08a5f28c statusexplanation8bce7960-4a30-4e42-ed88-30df08a5f28cDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation8bce7960-4a30-4e42-ed88-30df08a5f28c>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 4</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed2e54a89e-180f-4156-abfe-8b9bd62da9b4 Completed2e54a89e-180f-4156-abfe-8b9bd62da9b4Desc" xmlns=http://www.w3.org/2000/svg><title id=Completed2e54a89e-180f-4156-abfe-8b9bd62da9b4>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/lehkt/regularization","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/lehkt/regularization href=https://www.coursera.org/learn/machine-learning/exam/lehkt/regularization class=nostyle> <strong> Regularization </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanatione3a7fe8b-38ca-467d-c165-b72c7115a962 statusexplanatione3a7fe8b-38ca-467d-c165-b72c7115a962Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanatione3a7fe8b-38ca-467d-c165-b72c7115a962>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 4</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completede5610650-e955-4c65-f0d6-0f95c433b65f Completede5610650-e955-4c65-f0d6-0f95c433b65fDesc" xmlns=http://www.w3.org/2000/svg><title id=Completede5610650-e955-4c65-f0d6-0f95c433b65f>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/programming/ixFof/logistic-regression","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/programming/ixFof/logistic-regression href=https://www.coursera.org/learn/machine-learning/programming/ixFof/logistic-regression class=nostyle> <strong> Logistic Regression </strong> </a></div><div class=item-subtitle-text>Programming Assignment</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation47774eba-8864-4fa9-df1b-9cdb53fe8b89 statusexplanation47774eba-8864-4fa9-df1b-9cdb53fe8b89Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation47774eba-8864-4fa9-df1b-9cdb53fe8b89>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 4</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>8.33%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed0fb6a55a-8cb4-4015-83b0-23a3c7e029ca Completed0fb6a55a-8cb4-4015-83b0-23a3c7e029caDesc" xmlns=http://www.w3.org/2000/svg><title id=Completed0fb6a55a-8cb4-4015-83b0-23a3c7e029ca>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/HrMM9/neural-networks-representation","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/HrMM9/neural-networks-representation href=https://www.coursera.org/learn/machine-learning/exam/HrMM9/neural-networks-representation class=nostyle> <strong> Neural Networks: Representation </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation80f7f94a-9f45-4325-824c-044f3c4b06ba statusexplanation80f7f94a-9f45-4325-824c-044f3c4b06baDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation80f7f94a-9f45-4325-824c-044f3c4b06ba>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 11</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completedfd3eef17-ad98-4395-a8f1-b44235ed93a3 Completedfd3eef17-ad98-4395-a8f1-b44235ed93a3Desc" xmlns=http://www.w3.org/2000/svg><title id=Completedfd3eef17-ad98-4395-a8f1-b44235ed93a3>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/programming/Y54Zu/multi-class-classification-and-neural-networks","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/programming/Y54Zu/multi-class-classification-and-neural-networks href=https://www.coursera.org/learn/machine-learning/programming/Y54Zu/multi-class-classification-and-neural-networks class=nostyle> <strong> Multi-class Classification and Neural Networks </strong> </a></div><div class=item-subtitle-text>Programming Assignment</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanationdaebc0fa-2299-4b9d-c29a-df8e94368103 statusexplanationdaebc0fa-2299-4b9d-c29a-df8e94368103Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanationdaebc0fa-2299-4b9d-c29a-df8e94368103>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 11</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>8.33%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completedc720c61e-4aa8-4374-c7dc-1642825d3b1c Completedc720c61e-4aa8-4374-c7dc-1642825d3b1cDesc" xmlns=http://www.w3.org/2000/svg><title id=Completedc720c61e-4aa8-4374-c7dc-1642825d3b1c>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/3UQTb/neural-networks-learning","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/3UQTb/neural-networks-learning href=https://www.coursera.org/learn/machine-learning/exam/3UQTb/neural-networks-learning class=nostyle> <strong> Neural Networks: Learning </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation36bfd514-b0fd-47cb-cc70-93f7ca54129f statusexplanation36bfd514-b0fd-47cb-cc70-93f7ca54129fDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation36bfd514-b0fd-47cb-cc70-93f7ca54129f>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 18</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed02121956-0d40-42aa-f3d1-9937a3459e2f Completed02121956-0d40-42aa-f3d1-9937a3459e2fDesc" xmlns=http://www.w3.org/2000/svg><title id=Completed02121956-0d40-42aa-f3d1-9937a3459e2f>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/programming/AiHgN/neural-network-learning","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/programming/AiHgN/neural-network-learning href=https://www.coursera.org/learn/machine-learning/programming/AiHgN/neural-network-learning class=nostyle> <strong> Neural Network Learning </strong> </a></div><div class=item-subtitle-text>Programming Assignment</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation8bf36ca3-704e-4c9d-fa78-de1635718af7 statusexplanation8bf36ca3-704e-4c9d-fa78-de1635718af7Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation8bf36ca3-704e-4c9d-fa78-de1635718af7>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 18</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>8.33%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed428109ed-5c92-4114-bc0b-416e435c1fd8 Completed428109ed-5c92-4114-bc0b-416e435c1fd8Desc" xmlns=http://www.w3.org/2000/svg><title id=Completed428109ed-5c92-4114-bc0b-416e435c1fd8>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/w1FgU/advice-for-applying-machine-learning","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/w1FgU/advice-for-applying-machine-learning href=https://www.coursera.org/learn/machine-learning/exam/w1FgU/advice-for-applying-machine-learning class=nostyle> <strong> Advice for Applying Machine Learning </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation1019e178-fe7f-429c-efd1-037daf2a20ba statusexplanation1019e178-fe7f-429c-efd1-037daf2a20baDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation1019e178-fe7f-429c-efd1-037daf2a20ba>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 25</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>80%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed42f32b74-7f9b-47f3-bad4-7dba2ab93f68 Completed42f32b74-7f9b-47f3-bad4-7dba2ab93f68Desc" xmlns=http://www.w3.org/2000/svg><title id=Completed42f32b74-7f9b-47f3-bad4-7dba2ab93f68>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/programming/Im1UC/regularized-linear-regression-and-bias-variance","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/programming/Im1UC/regularized-linear-regression-and-bias-variance href=https://www.coursera.org/learn/machine-learning/programming/Im1UC/regularized-linear-regression-and-bias-variance class=nostyle> <strong> Regularized Linear Regression and Bias/Variance </strong> </a></div><div class=item-subtitle-text>Programming Assignment</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation973ec6af-3190-4815-c864-1995931dfba2 statusexplanation973ec6af-3190-4815-c864-1995931dfba2Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation973ec6af-3190-4815-c864-1995931dfba2>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 25</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>8.33%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed6ef3ab68-0281-4ce7-a6b3-d010b63f52e8 Completed6ef3ab68-0281-4ce7-a6b3-d010b63f52e8Desc" xmlns=http://www.w3.org/2000/svg><title id=Completed6ef3ab68-0281-4ce7-a6b3-d010b63f52e8>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/vrjOT/machine-learning-system-design","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/vrjOT/machine-learning-system-design href=https://www.coursera.org/learn/machine-learning/exam/vrjOT/machine-learning-system-design class=nostyle> <strong> Machine Learning System Design </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanationf55362f7-e118-46da-dc1a-464df1bbba2f statusexplanationf55362f7-e118-46da-dc1a-464df1bbba2fDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanationf55362f7-e118-46da-dc1a-464df1bbba2f>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>May 25</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>80%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completedd79a52cc-fb46-4965-e052-5c2717bd1fba Completedd79a52cc-fb46-4965-e052-5c2717bd1fbaDesc" xmlns=http://www.w3.org/2000/svg><title id=Completedd79a52cc-fb46-4965-e052-5c2717bd1fba>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/PLdSa/support-vector-machines","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/PLdSa/support-vector-machines href=https://www.coursera.org/learn/machine-learning/exam/PLdSa/support-vector-machines class=nostyle> <strong> Support Vector Machines </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation0c31442e-ef94-4581-a0bc-aab849121beb statusexplanation0c31442e-ef94-4581-a0bc-aab849121bebDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation0c31442e-ef94-4581-a0bc-aab849121beb>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 1</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed891c9bfe-8345-486b-e65d-0f33576faeb5 Completed891c9bfe-8345-486b-e65d-0f33576faeb5Desc" xmlns=http://www.w3.org/2000/svg><title id=Completed891c9bfe-8345-486b-e65d-0f33576faeb5>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/programming/e4hZk/support-vector-machines","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/programming/e4hZk/support-vector-machines href=https://www.coursera.org/learn/machine-learning/programming/e4hZk/support-vector-machines class=nostyle> <strong> Support Vector Machines </strong> </a></div><div class=item-subtitle-text>Programming Assignment</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanationb894b16a-9393-4a69-bd53-a6dfdd6d4b88 statusexplanationb894b16a-9393-4a69-bd53-a6dfdd6d4b88Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanationb894b16a-9393-4a69-bd53-a6dfdd6d4b88>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 1</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>8.33%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed89adb0a5-c02c-46a8-a157-986be616a6ab Completed89adb0a5-c02c-46a8-a157-986be616a6abDesc" xmlns=http://www.w3.org/2000/svg><title id=Completed89adb0a5-c02c-46a8-a157-986be616a6ab>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/4sGmv/unsupervised-learning","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/4sGmv/unsupervised-learning href=https://www.coursera.org/learn/machine-learning/exam/4sGmv/unsupervised-learning class=nostyle> <strong> Unsupervised Learning </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation3a309b0b-f8cb-4b8c-a6dd-72a333683030 statusexplanation3a309b0b-f8cb-4b8c-a6dd-72a333683030Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation3a309b0b-f8cb-4b8c-a6dd-72a333683030>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 8</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completeda41ea77e-1962-4590-9583-d385f40d0a55 Completeda41ea77e-1962-4590-9583-d385f40d0a55Desc" xmlns=http://www.w3.org/2000/svg><title id=Completeda41ea77e-1962-4590-9583-d385f40d0a55>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/B20Bx/principal-component-analysis","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/B20Bx/principal-component-analysis href=https://www.coursera.org/learn/machine-learning/exam/B20Bx/principal-component-analysis class=nostyle> <strong> Principal Component Analysis </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation218138ae-ce51-4225-f0d4-f6becbe77a4f statusexplanation218138ae-ce51-4225-f0d4-f6becbe77a4fDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation218138ae-ce51-4225-f0d4-f6becbe77a4f>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 8</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completede13d180a-337b-423c-fffd-e2e7dfab047e Completede13d180a-337b-423c-fffd-e2e7dfab047eDesc" xmlns=http://www.w3.org/2000/svg><title id=Completede13d180a-337b-423c-fffd-e2e7dfab047e>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/programming/ZZkM2/k-means-clustering-and-pca","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/programming/ZZkM2/k-means-clustering-and-pca href=https://www.coursera.org/learn/machine-learning/programming/ZZkM2/k-means-clustering-and-pca class=nostyle> <strong> K-Means Clustering and PCA </strong> </a></div><div class=item-subtitle-text>Programming Assignment</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation8f01a06a-95e6-4191-d857-5194d79a4760 statusexplanation8f01a06a-95e6-4191-d857-5194d79a4760Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation8f01a06a-95e6-4191-d857-5194d79a4760>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 8</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>8.33%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed0011c68b-d24a-43b5-dc02-497ffdc8d3e0 Completed0011c68b-d24a-43b5-dc02-497ffdc8d3e0Desc" xmlns=http://www.w3.org/2000/svg><title id=Completed0011c68b-d24a-43b5-dc02-497ffdc8d3e0>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/Dx28I/anomaly-detection","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/Dx28I/anomaly-detection href=https://www.coursera.org/learn/machine-learning/exam/Dx28I/anomaly-detection class=nostyle> <strong> Anomaly Detection </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation58749746-6bd2-4227-be3d-1d84be68c7d3 statusexplanation58749746-6bd2-4227-be3d-1d84be68c7d3Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation58749746-6bd2-4227-be3d-1d84be68c7d3>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 15</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completedce36fdd9-cc4d-4c99-be54-3f8d44f0f8a2 Completedce36fdd9-cc4d-4c99-be54-3f8d44f0f8a2Desc" xmlns=http://www.w3.org/2000/svg><title id=Completedce36fdd9-cc4d-4c99-be54-3f8d44f0f8a2>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/3HGvu/recommender-systems","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/3HGvu/recommender-systems href=https://www.coursera.org/learn/machine-learning/exam/3HGvu/recommender-systems class=nostyle> <strong> Recommender Systems </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation33bd7ff5-3e57-4279-fe6a-920cc1792e0a statusexplanation33bd7ff5-3e57-4279-fe6a-920cc1792e0aDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation33bd7ff5-3e57-4279-fe6a-920cc1792e0a>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 15</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>80%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completeddc41013f-7b4f-41e1-fed8-f9e049b1098b Completeddc41013f-7b4f-41e1-fed8-f9e049b1098bDesc" xmlns=http://www.w3.org/2000/svg><title id=Completeddc41013f-7b4f-41e1-fed8-f9e049b1098b>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/programming/fyhXS/anomaly-detection-and-recommender-systems","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/programming/fyhXS/anomaly-detection-and-recommender-systems href=https://www.coursera.org/learn/machine-learning/programming/fyhXS/anomaly-detection-and-recommender-systems class=nostyle> <strong> Anomaly Detection and Recommender Systems </strong> </a></div><div class=item-subtitle-text>Programming Assignment</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation7f9ae824-98cc-466a-fb9e-c9869d6b0c02 statusexplanation7f9ae824-98cc-466a-fb9e-c9869d6b0c02Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation7f9ae824-98cc-466a-fb9e-c9869d6b0c02>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 15</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>8.33%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed344ba342-9d83-48e1-b339-3759a707a7db Completed344ba342-9d83-48e1-b339-3759a707a7dbDesc" xmlns=http://www.w3.org/2000/svg><title id=Completed344ba342-9d83-48e1-b339-3759a707a7db>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/rpoYY/large-scale-machine-learning","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/rpoYY/large-scale-machine-learning href=https://www.coursera.org/learn/machine-learning/exam/rpoYY/large-scale-machine-learning class=nostyle> <strong> Large Scale Machine Learning </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanationf80730f4-5ef5-4724-decb-b12840b8b25e statusexplanationf80730f4-5ef5-4724-decb-b12840b8b25eDesc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanationf80730f4-5ef5-4724-decb-b12840b8b25e>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 22</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div><div class="rc-UngroupedNonPeerAssignmentRow rc-AssignmentsTableRow horizontal-box dark bordered" role=row><div class="item-column flex-1 horizontal-box"><div class="item-icon vertical-box align-items-absolute-center" aria-hidden=true><svg fill=#1F8354 class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px;margin-right:12px viewBox="0 0 48 48" role=img aria-labelledby="Completed34b1374c-5fb7-4d7f-c882-6d71f5999bba Completed34b1374c-5fb7-4d7f-c882-6d71f5999bbaDesc" xmlns=http://www.w3.org/2000/svg><title id=Completed34b1374c-5fb7-4d7f-c882-6d71f5999bba>Completed</title><path d="M1 24C1 11.318375 11.318375 1 24 1s23 10.318375 23 23-10.318375 23-23 23S1 36.681625 1 24zm20.980957 4.2558594l-7.7418213-7.0596924L12 23.5592041l9.980957 9.6016846 15.2832032-16.4852295L34.9130859 14 21.980957 28.2558594z" fill=#1F8354 role=presentation></path></svg></div><div class="item-column-text flex-1 vertical-box" role=gridcell><div class=item-title-text><a data-click-key=open_course_home.grades_page.click.grades_page_item_link data-click-value='{"href":"/learn/machine-learning/exam/lsjML/application-photo-ocr","namespace":{"action":"click","app":"open_course_home","component":"grades_page_item_link","page":"grades_page"},"open_course_slug":"machine-learning","schema_type":"FRONTEND"}' data-track=true data-track-app=open_course_home data-track-page=grades_page data-track-action=click data-track-component=grades_page_item_link data-track-href=/learn/machine-learning/exam/lsjML/application-photo-ocr href=https://www.coursera.org/learn/machine-learning/exam/lsjML/application-photo-ocr class=nostyle> <strong> Application: Photo OCR </strong> </a></div><div class=item-subtitle-text>Quiz</div></div></div><div class="status-column horizontal-box" role=gridcell><div class=status-column-icon><svg tabindex=0 aria-describedby=status-tooltip class=_ufjrdd style=fill:rgb(54,59,66);height:24px;width:24px viewBox="0 0 48 48" role=img aria-labelledby="statusexplanation7d21db00-ebc9-4e79-8323-32b853441d51 statusexplanation7d21db00-ebc9-4e79-8323-32b853441d51Desc" xmlns=http://www.w3.org/2000/svg><title id=statusexplanation7d21db00-ebc9-4e79-8323-32b853441d51>status explanation</title><path d="M24,0 C10.752,0 0,10.752 0,24 C0,37.248 10.752,48 24,48 C37.248,48 48,37.248 48,24 C48,10.752 37.248,0 24,0 Z M26.4,17 L21.6,17 L21.6,12.2 L26.4,12.2 L26.4,17 Z M26.4,36.4 L21.6,36.4 L21.6,22 L26.4,22 L26.4,36.4 Z" role=presentation></path></svg></div><div class=status-column-text>Locked</div></div><div class="due-column horizontal-box" role=gridcell><div class=due-column-icon></div><div class=due-column-text><div class=due-column-text-date>Jun 29</div><div class="_14m20gya due-column-text-time">1:59 PM +07</div></div></div><div class=weight-column role=gridcell><span>1.96%</span></div><div class=grade-column role=gridcell><strong> <span>100%</span> </strong></div></div></div></div></div></div></div></main><div><a data-click-key=open_course_home.help.click.icon 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aria-hidden=true></i></div></a></div></div></div></div></div><!--[if lte IE 9]><script>var queueScript = function(scripts, callback) {
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