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76,78,92,96,104,105,111,112,113,114,116,118,120,121,124,125,126,128,130,132,136,138,140,143,144,145,146,148,149,150,151,153,156,157,158,160,161,162,164,166,167,168,169,172,173,175,176,177,178,179,180,181,182,183,184,185,188,191,192,196,197,198,200,201,202,204,206,207,209,210,211,212,214,216,221,224],"null":[41,51,54,56,57,69,76,86,129,132,134,160,171,181,186,195,196,197],"office2016\u7b80\u4f53\u4e2d\u658764\u4f4d":41,"p\u03b8":142,"pikach\u00fa":12,"public":[1,14,59,65,66,111,118,124,126,128,148,152,164,166,168,169,191,198,201,205,206,207],"r\u00b2":195,"r\u00e9sum\u00e9":42,"return":[2,3,7,12,14,18,22,24,25,29,30,31,34,35,36,37,38,39,41,42,43,44,45,46,47,48,50,51,52,54,55,56,58,59,61,62,63,64,65,66,67,69,72,73,74,75,78,80,86,87,88,92,93,94,96,104,105,112,113,129,130,131,132,133,134,136,138,139,140,141,142,145,146,147,149,150,152,154,155,156,158,160,161,163,164,167,168,173,176,177,178,181,183,184,185,186,188,192,202,203,209,210,212,216,217,218,219,220,223,224],"short":[26,43,53,68,112,128,131,143,144,146,155,158,160,163,164,169,186,193,201,202,224],"static":[1,2,5,29,30,31,32,33,34,35,37,38,39,41,43,45,46,47,48,49,51,55,56,57,60,75,77,95,96,121,122,123,128,134,139,141,142,145,146,147,148,154,156,164,168,171,172,173,174,176,178,180,181,184,185,186,188,189,190,191,192,195,196,197,198,201,202,208,224],"super":[29,30,31,34,36,38,39,44,50,52,72,74,133,142,146,147,150,152,154,158,160,161,163,164,177,201,223],"switch":[0,7,14,55,59,167,173,201,223],"throw":202,"transient":165,"true":[1,7,9,14,18,22,24,29,30,31,33,34,35,36,37,38,39,40,41,43,44,45,46,47,48,49,51,52,54,55,56,57,58,59,60,61,62,63,64,65,66,67,69,71,73,75,76,78,79,80,86,88,89,90,92,94,95,104,108,109,112,113,120,121,128,129,131,132,133,134,136,139,140,142,144,145,146,147,149,151,152,153,154,156,158,160,161,163,164,168,169,171,173,174,175,176,180,181,182,183,184,185,186,190,192,195,196,197,198,200,201,202,203,208,209,212,214,218,222,223,224,225],"try":[1,3,4,5,7,9,11,14,16,18,25,31,33,37,38,40,42,51,53,55,58,59,60,61,62,63,65,66,67,68,69,70,71,72,73,74,75,80,88,94,95,101,104,112,113,114,116,119,120,121,122,123,126,129,130,132,133,134,136,141,147,150,151,152,154,160,161,163,164,167,168,169,171,172,176,177,178,180,181,182,183,184,185,186,187,189,190,191,195,196,197,198,199,200,202,203,216,221,224],"var":[18,41,60,64,78,92,120,142,152,163,164,173,176,196,208],"void":131,"wasn\u0645":164,"while":[0,1,7,29,31,32,33,34,38,42,46,47,54,55,56,59,62,66,67,68,69,70,73,87,97,105,111,113,114,115,116,118,120,123,124,125,126,128,129,131,132,133,134,136,139,140,144,146,147,158,160,161,165,167,169,176,182,183,184,185,186,189,191,195,196,197,202,204,205,209,210,211,212,216,217,221,222,224],"y\u00fcksek":37,A:[0,1,4,5,6,7,12,13,14,15,18,19,21,23,26,28,29,32,38,40,42,43,44,46,47,48,50,53,55,56,57,58,59,60,61,65,66,67,68,71,72,73,74,75,76,78,84,86,87,88,89,90,92,94,97,100,101,102,104,105,111,112,113,114,116,118,120,121,122,124,125,126,128,129,130,131,132,133,134,136,138,139,144,146,147,149,150,151,152,154,155,156,160,161,163,164,165,166,167,168,170,171,172,174,175,176,177,181,182,184,185,186,188,191,193,197,198,200,201,202,203,206,207,209,210,212,214,217,218,221,222,223,224,225],AND:[104,105,120,131,133,201,202,203],AS:[22,25,53,55,56,57,104,105,186,201,202,203],And:[31,32,33,46,48,50,56,58,59,61,65,67,71,76,78,86,88,90,92,104,112,114,116,124,128,131,138,143,144,145,147,150,155,158,160,165,166,167,168,169,173,182,184,192,198,202,206,210,214,217,224],As:[1,3,7,8,34,35,38,43,46,47,48,50,55,56,58,59,60,61,62,63,65,66,67,68,69,70,76,78,86,90,92,94,95,111,112,118,121,124,126,128,129,131,134,136,146,147,148,153,154,158,160,161,163,164,165,166,167,173,176,177,181,182,183,184,185,188,191,196,197,198,201,202,203,207,209,212,221,223,224],At:[28,42,46,56,59,65,68,78,87,92,118,128,131,136,144,145,164,166,167,168,169,173,177,178,183,191,198,200,201,202,209,221,222],BE:[104,105,201,202,203],BUT:[104,105,201,202,203],BY:[113,167,195],Be:[97,103,116,120,131,186],Being:[50,71,113,116,134],But:[34,41,42,43,46,47,50,56,58,59,61,62,65,66,67,68,70,73,78,86,92,112,116,121,124,136,141,143,149,151,153,156,160,165,166,167,172,176,177,178,180,182,183,184,185,191,197,200,201,202,203,214,218],By:[7,18,29,43,46,48,54,58,61,62,63,66,68,78,86,88,89,90,92,111,113,121,126,129,131,134,136,142,156,158,160,162,165,166,168,171,173,174,181,183,184,188,191,192,195,196,197,201,212],FOR:[42,104,105,201,202,203],For:[7,19,29,30,31,32,33,37,38,41,42,43,45,46,47,48,50,53,54,55,56,58,59,60,63,68,69,70,73,75,76,77,78,79,81,83,84,86,87,96,97,98,100,101,102,103,112,113,114,123,124,125,126,128,129,131,132,133,134,138,143,144,147,151,152,156,158,160,161,163,164,165,166,167,168,169,171,172,173,174,176,177,178,181,182,183,184,185,186,188,189,190,191,192,196,197,198,200,201,202,203,204,214,218,221,222,224],IN:[25,94,104,105,149,164,201,202,203],IS:[22,53,55,56,57,63,104,105,109,160,171,201,202,203],IT:[63,111,166],If:[1,7,14,16,18,29,30,33,34,35,37,42,45,46,47,48,50,52,53,56,58,59,60,61,62,63,66,67,68,69,71,73,75,78,81,86,88,89,92,94,104,105,107,112,113,115,116,118,120,121,122,123,124,125,126,128,129,130,131,132,133,134,136,138,142,143,144,146,148,150,154,158,160,161,164,165,166,167,169,171,173,176,177,178,180,182,183,184,185,186,188,190,191,192,195,196,197,198,200,201,202,203,210,216,218,221,223,224,225],In:[1,3,7,8,9,11,12,13,14,16,18,19,21,24,28,29,30,31,32,33,34,38,40,42,43,44,45,46,47,48,50,52,53,54,55,56,58,59,61,62,63,66,67,68,69,70,71,73,75,76,78,81,83,86,87,88,89,90,92,94,95,100,101,102,104,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,136,137,138,139,140,142,143,144,145,146,147,148,149,150,151,152,153,156,158,159,160,161,162,163,164,165,166,167,168,169,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,207,209,210,211,216,218,219,220,221,222,224,225],Is:[59,105,109,111,115,118,123,124,125,142,150,151,161,166,167,171,186,191,197,198,225],It:[0,1,2,3,4,5,7,8,9,10,11,13,14,15,16,17,18,19,20,23,24,26,27,28,29,30,31,32,33,34,35,36,37,38,39,41,42,43,45,46,47,48,49,50,51,52,54,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,80,81,83,84,86,88,89,90,92,94,95,96,97,98,100,101,102,103,111,112,113,114,115,116,118,120,121,122,123,124,125,126,128,129,130,131,132,133,134,139,140,141,142,143,144,146,147,148,149,150,151,152,153,154,155,156,158,160,161,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,188,189,190,191,192,193,195,196,198,200,201,202,203,205,206,212,216,217,218,219,221,222,224],Its:[143,145,151,161,176,181,197],NEAR:[70,86],NO:[104,105,201,202,203],NOT:[94,104,105,131,145,148,158,160,201,202,203],Near:[121,208],No:[7,20,29,34,43,59,63,65,73,80,94,100,105,110,112,113,122,144,161,173,182,183,184,189,201,202,205,208],Not:[7,46,50,58,61,63,65,78,92,113,123,129,130,154,164,177,189,196,197,202,217,224],OF:[22,42,53,55,56,57,104,105,145,201,202,203],ON:210,ONE:[7,42],OR:[22,53,55,56,57,104,105,131,201,202,203],Of:[33,59,76,113,114,116,126,183,184,203,204],On:[42,58,59,61,66,67,68,69,70,75,76,78,86,92,95,113,116,118,151,161,167,175,176,177,180,181,182,183,184,185,186,191,195,196,200,201,207,217],One:[1,7,11,28,42,43,46,50,58,59,61,62,63,64,66,67,68,75,95,110,115,116,118,120,122,126,128,131,139,143,152,158,161,162,167,172,174,176,182,185,191,196,198,201,202,203,207,208,209,214,216,221,224],Or:[32,33,46,59,67,86,114,116,131,143,144,167,171,174,183,191,201,202,217,221,224],Such:[1,7,30,46,50,58,59,63,128,145,150,167,168,196,201,223],THAT:94,THE:[42,104,105,201,202,203],TO:[42,63,104,105,201,202,203],That:[31,32,46,50,56,58,59,61,66,70,71,76,78,86,92,116,121,128,131,136,144,153,160,161,164,172,174,177,178,184,186,191,192,197,202,203,221],The:[0,3,5,6,7,8,12,13,14,15,16,18,19,24,25,26,28,29,30,31,32,33,34,35,36,37,39,42,43,44,45,46,47,48,52,53,54,55,56,57,58,59,61,63,64,65,66,67,68,69,70,71,72,73,74,75,76,78,83,86,88,90,92,95,96,102,103,104,105,111,114,115,116,118,120,121,122,124,125,126,127,128,130,131,132,133,134,138,139,140,141,142,143,144,145,146,147,148,150,152,154,155,156,157,160,162,163,164,165,166,167,168,169,171,172,173,174,175,176,177,178,179,180,181,182,185,186,188,189,191,192,193,195,196,197,203,204,206,207,208,212,213,215,216,217,218,219,220,221,222,225],Their:[145,151],Then:[7,31,42,47,53,55,59,65,75,89,94,113,116,128,131,139,142,148,149,150,154,155,158,160,164,167,168,169,172,173,174,175,176,177,181,183,185,186,196,200,201,202,203,217],There:[0,1,3,7,18,28,29,30,32,33,35,38,39,42,43,44,45,46,48,50,54,55,57,58,59,60,63,66,68,69,71,73,76,78,84,86,92,97,112,113,116,118,120,121,122,125,126,128,129,130,131,132,136,138,139,140,143,146,148,150,151,154,155,156,161,164,165,166,167,168,169,171,172,174,176,177,180,183,184,185,186,187,188,190,191,192,196,197,199,200,201,202,203,221,222,223,224],These:[7,30,31,33,40,42,45,47,48,52,53,58,63,65,68,69,76,86,87,89,90,111,113,116,118,121,124,125,130,131,133,134,146,148,150,151,153,158,161,162,164,166,168,172,176,177,181,183,184,185,195,201,203,205,207,209,210,212,223],To:[0,1,7,14,18,22,29,30,33,34,35,38,40,42,43,46,47,48,53,54,56,58,59,61,63,67,70,71,75,76,78,86,87,88,94,104,111,112,113,114,116,121,122,123,124,126,128,129,131,132,134,136,141,142,143,146,147,149,154,156,157,158,161,162,164,165,166,167,168,169,173,174,177,178,180,181,182,184,185,186,188,191,192,196,197,198,200,201,202,203,207,208,210,213,216,217,221,223,224,225],WITH:[104,105,201,202,203],Will:[169,197,203],With:[7,43,44,46,48,50,55,59,65,69,70,71,96,111,115,116,120,121,123,124,125,126,130,131,133,134,136,143,145,161,164,166,167,174,176,177,182,184,185,191,193,201,210,217,222],_0:177,_1:[174,183],_2:[158,174,183],_:[18,29,31,33,34,36,39,40,47,48,60,65,80,93,138,139,142,144,145,146,147,154,155,156,158,160,161,163,164,174,177,184,192,201,202,212,218,219,224],____:[3,12,22,24,25,54,104,105,109],_____:[24,104],______:[12,14,25],_______:14,________:14,_________:14,_____________________________________________________________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,214,215,222,225],complex_numb:201,complex_number_1:[202,224],complex_number_2:[202,224],complex_number__1:224,complexnumb:201,complexnumberwithconstructor:201,compli:124,complianc:[22,53,55,56,57,124,206],compliant:124,complic:[38,59,94,126,131,151,158,165,166,167,177,180,183,184,198,212],compon:[36,43,52,76,87,88,113,114,120,130,138,144,150,156,162,165,166,167,168,173,177,180,182,203,204],components_:[184,185,216],compos:[38,39,52,70,86,94,138,148,161,165,166,177,185,218],compose_greet_func:201,compose_greet_func_with_closur:201,composit:[43,131,145,176],compound:[202,209,224],compound_stmt:201,comprehend:[58,162],comprehens:[105,120,134,148,151,162,167,183,196],compress:[29,30,31,118,138,143,146],compris:[45,52,113,162,166],compromis:[7,129],compulsori:42,comput:[3,7,18,22,32,33,34,38,42,43,46,48,50,52,54,55,58,59,62,63,67,68,75,80,87,88,89,93,94,96,111,114,115,118,126,128,129,130,132,133,134,138,141,143,146,148,149,150,151,152,155,157,160,161,162,163,164,165,166,167,169,173,174,176,177,181,182,183,185,188,191,192,197,200,202,204,205,206,217,218,219,221,224,225],computation:[34,38,42,59,65,131,133,143,146,156,163,164,179],computationn:34,compute_reciproc:209,compute_target:[9,112],con:[7,55,65,113,124],concat:[22,30,36,38,41,49,52,63,65,75,142,146,147,151,152,154,156,188,201,202],concat_axi:132,concat_index:132,concaten:[35,36,41,43,52,64,88,131,132,142,147,151,152,158,161,198,202,212,218,219,220,224],concatenated_str:201,concav:142,conceiv:[168,201],concentr:171,concept:[3,18,29,31,33,42,55,59,68,87,88,89,113,114,126,128,130,131,134,139,146,157,163,164,165,166,167,168,169,174,177,181,182,183,189,194,195,197,200,202,211,224],conceptu:[158,160,177],concern:[7,47,55,63,67,68,76,86,118,121,124,129,148,162,165,166,169,177,183,196,208,221],concis:[43,131,173,191,201,202,224],conclud:[65,68,76,87,114,120,128,161,167,174,185],conclus:[24,59,115,124,126,196],concret:[158,169,183,185,191,216],concurr:[96,112,113],conda:[0,30,37,38,121,155],condens:[144,158],condit:[3,22,31,45,46,53,55,56,57,59,63,105,114,124,131,142,146,151,160,162,164,167,174,176,177,185,196,197,198,201,202,203,223,224],condition2:63,condorcet:175,conduct:[65,112,124,183,185,206],conf:18,conf_matrix:[61,66],confer:[116,120,132],confid:[33,46,48,56,78,92,123,146,164,167,168,171,173,177],config:[9,41,59,75,80,154,156,160,173,176,185,216,217],configur:[10,42,43,48,53,55,111,113,155,160,163,164,165,166,167,191,198,199,201],configure_optim:161,confirm:[14,30,53,55,56,68,115,118,124,184,196,206,207],conflict:[105,116,124,134],conform:[125,134,162,165,167],confus:[7,35,46,59,61,66,69,78,92,95,116,129,131,151,161,174,176,181,188,201],confusingli:184,confusion_matrix:[35,45,46,60,61,66,68,69,78,79,92,95,96,151,183,189,190,192,197,219,220],confusion_mtx:35,congratul:[112,113,196,197,200,203],conj:160,conjug:104,conjunct:[126,160],conjunction_list:160,conjur:145,connect:[6,30,32,33,34,36,44,48,50,53,56,71,94,104,105,114,116,124,128,146,147,148,151,155,161,164,167,183,201,202,203,212],connectionist:90,conquer:177,consciou:7,consecut:[14,32,46,58,161,181],consent:[124,206],consequ:[28,114,124,144,160,175,206],conserv:[121,208],conservationstatu:[121,208],consid:[1,3,7,8,11,14,18,22,24,29,38,42,43,45,46,48,53,54,58,59,62,65,71,73,87,88,105,113,115,116,118,120,125,126,127,128,129,138,139,144,146,148,149,150,151,153,154,156,158,161,163,164,166,167,168,169,171,173,174,175,176,177,178,181,182,183,184,185,186,191,197,200,201,202,214,216,223,224],consider:[42,68,76,113,118,124,126,127,134,162,177,180,184,206,207],consist:[0,1,3,8,15,30,33,34,35,38,42,48,52,53,58,59,61,63,68,100,118,124,129,131,138,146,147,150,158,161,162,164,165,166,167,168,169,174,183,185,191,196,198,202,206,207,210,221],consol:134,consolid:87,conspiraci:71,constant:[52,59,72,74,87,90,131,140,141,142,144,145,146,151,167,177,181,192,195],constant_initi:152,constantli:126,constitut:[151,161],constrain:[30,144,171,183],constraint:[30,113,118,131,146,166,167,171,183,185,189,216],construct:[30,44,59,131,134,138,142,145,146,151,156,165,171,173,176,177,178,181,201,202,224],constructor:[52,134,146,201,202,223,224],consult:[7,132],consum:[10,20,48,53,110,112,113,115,120,123,124,125,165,167,169,182,183,186,191,205],consumpt:[120,186,205,209],cont:63,cont_num_var:63,contact:[161,210,217],contagi:197,contain:[1,3,6,7,12,14,15,17,22,29,31,33,34,38,39,42,44,45,46,47,48,52,53,54,55,56,58,59,60,61,63,66,68,69,71,78,86,92,95,98,103,104,105,112,113,115,118,125,128,129,130,132,134,136,143,146,147,148,150,151,155,158,161,162,164,165,166,167,171,173,175,176,178,181,183,184,186,188,191,195,196,197,198,200,201,202,209,223,224],container:166,content:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,35,36,37,38,39,40,41,42,43,45,46,47,48,49,50,51,54,57,58,59,60,61,62,63,64,65,68,71,72,73,74,75,76,77,78,81,83,84,86,92,94,95,96,97,98,100,101,102,103,111,112,113,114,115,116,118,120,121,122,123,124,125,126,128,129,130,131,132,133,134,136,138,139,140,141,142,144,145,146,147,148,149,150,151,1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class=\"section-number\">42. </span>Self-paced assignments","<span class=\"section-number\">42.22. </span>Analyzing COVID-19 papers","<span class=\"section-number\">42.27. </span>Analyzing data","<span class=\"section-number\">42.9. </span>Analyzing text about Data Science","<span class=\"section-number\">42.13. </span>Apply your skills","<span class=\"section-number\">42.16. </span>Build your own custom vis","<span class=\"section-number\">42.17. </span>Classifying datasets","<span class=\"section-number\">42.26. </span>Data preparation","<span class=\"section-number\">42.24. </span>Data processing in Python","<span class=\"section-number\">42.41. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">42.40. </span>Data Science project using Azure ML SDK","<span class=\"section-number\">42.10. </span>Data Science scenarios","<span class=\"section-number\">42.20. </span>Displaying airport data","<span class=\"section-number\">42.15. </span>Dive into the beehive","<span class=\"section-number\">42.23. </span>Estimation of COVID-19 pandemic","<span class=\"section-number\">42.25. </span>Evaluating data from a form","<span class=\"section-number\">42.36. </span>Explore a planetary computer dataset","<span class=\"section-number\">42.37. </span>Exploring for answers","<span class=\"section-number\">42.19. </span>Introduction to probability and statistics","<span class=\"section-number\">42.12. </span>Lines, scatters and bars","<span class=\"section-number\">42.39. </span>Low code/no code Data Science project on Azure ML","<span class=\"section-number\">42.38. </span>Market research","<span class=\"section-number\">42.29. </span>Matplotlib applied","<span class=\"section-number\">42.28. </span>NYC taxi data in winter and summer","<span class=\"section-number\">42.18. </span>Small diabetes study","<span class=\"section-number\">42.21. </span>Soda profits","<span class=\"section-number\">42.35. </span>Tell a story","<span class=\"section-number\">42.14. </span>Try it in Excel","<span class=\"section-number\">42.11. </span>Write a data ethics case study","<span class=\"section-number\">42.106. </span>Intro to Autoencoders","<span class=\"section-number\">42.107. </span>Base/Denoising Autoencoder &amp; Dimension Reduction","<span class=\"section-number\">42.108. </span>Fun with Variational Autoencoders","<span class=\"section-number\">42.94. </span>How to choose cnn architecture mnist","<span class=\"section-number\">42.99. </span>Image classification","<span class=\"section-number\">42.98. </span>Object Recognition in Images using CNN","<span class=\"section-number\">42.96. </span>Sign Language Digits Classification with CNN","<span class=\"section-number\">42.122. </span>Summary","<span class=\"section-number\">42.117. </span>DQN On Foreign Exchange Market","<span class=\"section-number\">42.118. </span>Art by gan","<span class=\"section-number\">42.120. </span>Generative Adversarial Networks (GANs)","<span class=\"section-number\">42.121. </span>Comparing edge-based and region-based segmentation","<span class=\"section-number\">42.102. </span>Bitcoin LSTM Model with Tweet Volume and Sentiment","<span class=\"section-number\">42.128. </span>Beginner\u2019s Guide to Text Pre-Processing","<span class=\"section-number\">42.126. </span>Getting Start NLP with classification task","<span class=\"section-number\">42.129. </span>News topic classification tasks","<span class=\"section-number\">42.112. </span>NN Classify 15 Fruits Assignment","<span class=\"section-number\">42.111. </span>Neural Networks for Classification with TensorFlow","<span class=\"section-number\">42.123. </span>Car Object Detection","<span class=\"section-number\">42.125. </span>Basic classification: Classify images of clothing","<span class=\"section-number\">42.104. </span>Google Stock Price Prediction RNN","<span class=\"section-number\">42.100. </span>Intro to TensorFlow for Deep Learning","<span class=\"section-number\">42.109. </span>Time Series Forecasting Assignment","<span class=\"section-number\">42.130. </span>Complete the transformer architecture","<span class=\"section-number\">42.86. </span>Counterintuitive Challenges in ML Debugging","<span class=\"section-number\">42.85. </span>Data engineering","<span class=\"section-number\">42.87. </span>Case Study: Debugging in Classification","<span class=\"section-number\">42.88. </span>Case Study: Debugging in Regression","<span class=\"section-number\">42.89. </span>Introduction","<span class=\"section-number\">42.78. </span>Beyond random forests: more ensemble models","<span class=\"section-number\">42.79. </span>Decision trees","<span class=\"section-number\">42.83. </span>Random Forest Classifier with Feature Importance","<span class=\"section-number\">42.77. </span>Random forests for classification","<span class=\"section-number\">42.76. </span>Random forests intro and regression","<span class=\"section-number\">42.82. </span>Boosting with tuning","<span class=\"section-number\">42.81. </span>Gradient boosting","<span class=\"section-number\">42.80. </span>Hyperparameter tuning gradient boosting","<span class=\"section-number\">42.69. </span>Decision Trees - Classification","<span class=\"section-number\">42.68. </span>Decision Trees - Intro and Regression","<span class=\"section-number\">42.65. </span>Kernel method assignment 1","<span class=\"section-number\">42.67. </span>Support Vector Machines (SVM) - Classification","<span class=\"section-number\">42.66. </span>Support Vector Machines (SVM) - Intro and SVM for Regression","<span class=\"section-number\">42.72. </span>Dropout and Batch Normalization","<span class=\"section-number\">42.73. </span>Lasso and Ridge Regression","<span class=\"section-number\">42.71. </span>Learning Curve To Identify Overfit &amp; Underfit","<span class=\"section-number\">42.70. </span>Model selection assignment 1","<span class=\"section-number\">42.74. </span>Regularized Linear Models","<span class=\"section-number\">42.75. </span>Customer segmentation: clustering - assignment 3","<span class=\"section-number\">42.92. </span>Build Classification Model","<span class=\"section-number\">42.91. </span>Build classification models","<span class=\"section-number\">42.53. </span>Build ML web app - assignment 1","<span class=\"section-number\">42.54. </span>Build ML web app - assignment 2","<span class=\"section-number\">42.59. </span>Create a regression model","<span class=\"section-number\">42.63. </span>Delicious asian and indian cuisines","<span class=\"section-number\">42.64. </span>Explore classification methods","<span class=\"section-number\">42.57. </span>Exploring visualizations","<span class=\"section-number\">42.60. </span>Linear and polynomial regression","<span class=\"section-number\">42.45. </span>Linear regression - California Housing","<span class=\"section-number\">42.48. </span>Gradient descent","<span class=\"section-number\">42.49. </span>Linear Regression Implementation from Scratch","<span class=\"section-number\">42.46. </span>Linear Regression Metrics","<span class=\"section-number\">42.47. </span>Loss Function","<span class=\"section-number\">42.56. </span>Managing data","<span class=\"section-number\">42.50. </span>ML logistic regression - assignment 1","<span class=\"section-number\">42.51. </span>ML logistic regression - assignment 2","<span class=\"section-number\">42.52. </span>ML neural network - Assignment 1","<span class=\"section-number\">42.42. </span>Machine Learning overview - assignment 1","<span class=\"section-number\">42.43. </span>Machine Learning overview - assignment 2","<span class=\"section-number\">42.93. </span>Parameter play","<span class=\"section-number\">42.62. </span>Pumpkin varieties and color","<span class=\"section-number\">42.55. </span>Regression tools","<span class=\"section-number\">42.44. </span>Regression with Scikit-learn","<span class=\"section-number\">42.61. </span>Retrying some regression","<span class=\"section-number\">42.90. </span>Study the solvers","<span class=\"section-number\">42.58. </span>Try a different model","<span class=\"section-number\">42.8. </span>Python programming advanced","<span class=\"section-number\">42.7. </span>Python programming basics","<span class=\"section-number\">42.6. </span>Python programming introduction","<span class=\"section-number\">42.5. </span>Project Plan\u200b Template","<span class=\"section-number\">42.3. </span>First assignment","<span class=\"section-number\">42.4. </span>Second assignment","<span class=\"section-number\">8. </span>Data Science in the cloud","<span class=\"section-number\">8.1. </span>Introduction","<span class=\"section-number\">8.3. </span>Data Science in the cloud: The \u201cAzure ML SDK\u201d way","<span class=\"section-number\">8.2. </span>The \u201clow code/no code\u201d way","<span class=\"section-number\">9. </span>Data Science in the real world","<span class=\"section-number\">7.2. </span>Analyzing","<span class=\"section-number\">7.3. </span>Communication","<span class=\"section-number\">7. </span>Data Science lifecycle","<span class=\"section-number\">7.1. </span>Introduction to the Data Science lifecycle","<span class=\"section-number\">6. </span>Data visualization","<span class=\"section-number\">6.4. </span>Making meaningful visualizations","<span class=\"section-number\">6.1. </span>Visualizing distributions","<span class=\"section-number\">6.2. </span>Visualizing proportions","<span class=\"section-number\">6.3. </span>Visualizing relationships: all about honey \ud83c\udf6f","<span class=\"section-number\">4.2. </span>Data Science ethics","<span class=\"section-number\">4.3. </span>Defining data","<span class=\"section-number\">4.1. </span>Defining data science","<span class=\"section-number\">4. </span>Introduction","<span class=\"section-number\">4.4. </span>Introduction to statistics and probability","<span class=\"section-number\">5.5. </span>Data preparation","<span class=\"section-number\">5.2. </span>Non-relational data","<span class=\"section-number\">5.3. </span>NumPy","<span class=\"section-number\">5.4.3. </span>Advanced Pandas Techniques","<span class=\"section-number\">5.4.2. </span>Data Selection","<span class=\"section-number\">5.4.1. </span>Introduction and Data Structures","<span class=\"section-number\">5.4. </span>Pandas","<span class=\"section-number\">5.1. </span>Relational databases","<span class=\"section-number\">5. </span>Working with data","<span class=\"section-number\">26. </span>Autoencoder","<span class=\"section-number\">23. </span>Convolutional Neural Networks","<span class=\"section-number\">23.10. </span>Deepdream in TensorFlow","<span class=\"section-number\">23.9. </span>Stylenet / Neural-Style","<span class=\"section-number\">32. </span>Diffusion Model","<span class=\"section-number\">21. </span>Intro to Deep Learning","<span class=\"section-number\">33. </span>Deep Q-learning","<span class=\"section-number\">31. </span>Generative adversarial networks","<span class=\"section-number\">28. </span>Image classification","<span class=\"section-number\">29. </span>Image segmentation","<span class=\"section-number\">30. </span>Natural Language Processing","<span class=\"section-number\">30.7.1. </span>Text Preprocessing","<span class=\"section-number\">30.7.2. </span>Word embedding","<span class=\"section-number\">22. </span>Neural Networks","<span class=\"section-number\">27. </span>Object detection","<span class=\"section-number\">24.1.2. </span>Bidirectional RNN","<span class=\"section-number\">24.1.1. </span>Long-short term memory","<span class=\"section-number\">24. </span>Recurrent Neural Networks","<span class=\"section-number\">25. </span>Time series","Learn AI together, for free","<span class=\"section-number\">40.1. </span>Coding Attention Mechanisms","<span class=\"section-number\">40. </span>Large Language Models Basic","<span class=\"section-number\">40.3. </span>Transformers for Language Modelling","<span class=\"section-number\">40.2. </span>Transformer","<span class=\"section-number\">39. </span>Introduction","<span class=\"section-number\">41. </span>Implementing a GPT model from Scratch To Generate Text","<span class=\"section-number\">41.10. </span>Pretraining on Unlabeled Data","<span class=\"section-number\">36. </span>Data engineering","<span class=\"section-number\">38. </span>Model deployment","<span class=\"section-number\">37. </span>Model training &amp; evaluation","<span class=\"section-number\">34. </span>Overview","<span class=\"section-number\">35. </span>Problem framing","<span class=\"section-number\">14. </span>Clustering models for Machine Learning","<span class=\"section-number\">14.1. </span>Introduction to clustering","<span class=\"section-number\">14.2. </span>K-Means clustering","<span class=\"section-number\">15.2. </span>Bagging","<span class=\"section-number\">15.4. </span>Feature importance","<span class=\"section-number\">15. </span>Getting started with ensemble learning","<span class=\"section-number\">15.3. </span>Random forest","<span class=\"section-number\">16.1. </span>Gradient Boosting","<span class=\"section-number\">16.2. </span>Gradient boosting example","<span class=\"section-number\">16. </span>Introduction to Gradient Boosting","<span class=\"section-number\">16.3. </span>XGBoost","<span class=\"section-number\">16.4. </span>XGBoost + k-fold CV + Feature Importance","<span class=\"section-number\">18. </span>Kernel method","<span class=\"section-number\">20. </span>Model selection","<span class=\"section-number\">17. </span>Unsupervised learning","<span class=\"section-number\">19. </span>Unsupervised learning: PCA and clustering","<span class=\"section-number\">12.4. </span>Build a web app to use a Machine Learning model","<span class=\"section-number\">12. </span>Getting started with classification","<span class=\"section-number\">12.1. </span>Introduction to classification","<span class=\"section-number\">12.2. </span>More classifiers","<span class=\"section-number\">12.3. </span>Yet other classifiers","<span class=\"section-number\">10. </span>Machine Learning overview","<span class=\"section-number\">13.2. </span>Gradient descent","<span class=\"section-number\">13.1. </span>Loss function","<span class=\"section-number\">13. </span>Parameter Optimization","<span class=\"section-number\">11.5. </span>Univariate linear regression","<span class=\"section-number\">11.3. </span>Linear and polynomial regression","<span class=\"section-number\">11.4. </span>Logistic regression","<span class=\"section-number\">11.2. </span>Managing data","<span class=\"section-number\">11. </span>Regression models for Machine Learning","<span class=\"section-number\">11.1. </span>Tools of the trade","<span class=\"section-number\">3. </span>Python programming advanced","<span class=\"section-number\">2. </span>Python programming basics","<span class=\"section-number\">1. </span>Python programming introduction","<span class=\"section-number\">43.10. </span>Data Science in real world","<span class=\"section-number\">43.9. </span>Data Science in the cloud","<span class=\"section-number\">43.4. </span>Data Science introduction","<span class=\"section-number\">43.8. </span>Data Science lifecycle","<span class=\"section-number\">43.7. </span>Data visualization","<span class=\"section-number\">43.6. </span>NumPy and Pandas","<span class=\"section-number\">43.5. </span>Relational vs. non-relational database","<span class=\"section-number\">43.20. </span>Convolutional Neural Network","<span class=\"section-number\">43.21. </span>Generative Adversarial Network","<span class=\"section-number\">43. </span>Slides","<span class=\"section-number\">43.18. </span>Kernel method","<span class=\"section-number\">43.19. </span>Model Selection","<span class=\"section-number\">43.17. </span>Unsupervised learning","<span class=\"section-number\">43.16. </span>Build an machine learning web application","<span class=\"section-number\">43.12. </span>Linear Regression","<span class=\"section-number\">43.13. </span>Logistic Regression","<span class=\"section-number\">43.14. </span>Logistic Regression","<span class=\"section-number\">43.11. </span>Machine Learning overview","<span class=\"section-number\">43.15. </span>Neural Network","<span class=\"section-number\">43.3. </span>Python programming advanced","<span class=\"section-number\">43.2. </span>Python programming basics","<span class=\"section-number\">43.1. </span>Python programming introduction"],titleterms:{"0":[68,139],"1":[3,24,30,32,50,58,59,61,62,63,65,66,67,68,69,70,74,78,79,86,87,92,94,95,116,125,131,139,145,146,185,204,205,206,207,208,209,210,211,212,214,218,220,224,225],"10":[46,65,139,146],"100":[60,68,146],"1000":[68,146],"11":65,"12":65,"13":65,"1300131294":182,"15":45,"19":[1,8,14,141],"1d":131,"2":[3,24,30,32,50,51,58,59,60,61,62,63,65,66,67,69,70,78,80,87,92,93,96,116,125,131,185,204,205,206,207,208,209,210,211,212,214,218,220,224,225],"2d":[30,131,214],"3":[3,24,32,45,50,58,59,61,62,63,65,66,67,69,70,76,78,86,92,116,131,204,205,206,207,208,209,211,212,218,224,225],"3d":[30,95,120,214],"4":[3,24,32,50,58,59,60,61,62,63,65,66,67,69,70,78,92,116,185,204,205,206,207,208,209,210,224,225],"4d":139,"5":[24,32,50,58,59,62,63,65,66,67,69,70,78,92,95,116,146,185,204,205,206,207,208,210,224,225],"50":65,"500":65,"6":[50,59,61,62,63,65,66,67,69,70,78,92,204,205,206,207,208],"7":[50,59,61,62,65,66,67,69,70,204,207,208],"8":[61,65,207],"9":65,"90":182,"abstract":192,"boolean":[131,202,203,224,225],"break":[104,201,223],"case":[28,53,55,56,59,116,124,198,214],"class":[37,45,55,59,60,72,74,104,158,193,201,223],"default":[60,68,141,201],"do":[55,126,160,162,197,198,202,206,214],"final":[58,80,86,87,192],"float":[7,202,224],"function":[44,50,60,68,87,88,90,94,104,105,131,139,143,144,145,164,177,192,193,201,203,209,219,220,222,223],"import":[9,29,34,39,43,48,51,57,58,60,61,62,63,65,66,67,68,69,70,73,75,76,95,129,141,142,167,174,181,195,200,201,218,219,220,222],"long":[154,158],"new":[33,44,65,106,131,134,219,220],"null":[7,68,209],"public":39,"return":[106,144,201],"short":154,"static":182,"true":68,"try":[0,27,56,78,87,92,103,201,223],"while":[104,201,223],A:[31,33,143,158,162,183,189,190,196],And:191,At:[54,192,193],But:198,By:177,For:[104,148],Is:169,It:[136,197,210],NOT:197,Not:191,Of:149,On:[37,160],One:[57,86,87,150,222],That:219,The:[9,38,50,60,62,87,89,94,112,113,129,136,151,158,161,183,184,190,198,200,201,202,205,209,210,211,214,223,224],There:198,To:[73,163,183],With:[35,156,192],about:[3,34,123,188,197,208],absolut:[89,193],acceler:184,accent:42,access:[105,131,134],accordingli:139,accuraci:[32,48,55,68,139,146,181,185,222],acknowledg:[1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,45,46,47,48,49,50,51,54,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,81,83,84,86,92,94,95,96,97,98,100,101,102,103,104,105,111,112,113,114,115,116,118,120,121,122,123,124,125,126,128,129,130,131,132,133,134,136,138,139,140,141,142,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,160,161,163,164,167,171,172,173,174,175,176,177,178,180,181,182,183,186,188,189,190,191,195,196,197,198,200,201,202,203,211,214,216,217,218,219,222],ackowledg:212,action:144,activ:[143,145,163],actual:116,ad:[30,31,55,71,75,131,163,183],adaboost:58,adam:222,add:[56,131,167],addit:[134,158],advanc:[32,104,131,132,201,223],advantag:150,adversari:[39,145,212],after:[30,183,215],ag:24,against:56,agent:[37,144],agglom:[184,185],aggreg:[131,132,206,209],ahead:94,ai:[38,48,157,164,216],airport:12,aka:73,algorithm:[59,63,144,168,171,176,177,181,184],align:134,all:[24,42,55,86,123,136,158,196,208,210],alphanumer:42,an:[9,31,73,75,112,113,116,130,131,163,164,186,211,217],anagram:202,analysi:[8,41,58,60,61,62,63,65,66,67,68,69,70,86,89,111,115,185,191,216,221],analyz:[1,2,3,18,115,131,198,207],anchor:[45,60],anim:120,ann:51,annot:[201,223],anomal:55,anomali:[29,161],anoth:39,answer:17,ap:182,api:[35,50],app:[79,80,186],append:[105,209],appli:[4,22,55,124,158,189,190,206],applic:[59,138,148,185,217,221],approach:[60,116,136,189,210],ar:[162,202],arbitrari:201,architectur:[32,52,139,143,151,161,162,163,211,222],argument:[141,201,223],arithmet:[134,202,224],arrai:[131,134,209],art:38,artifici:221,artwork:38,ascend:[105,106],asian:82,ask:198,assert:60,assign:[0,12,25,45,51,68,74,76,79,80,92,93,94,95,96,108,109,131,132,134,185,220],assist:147,attend:158,attent:[52,158,163],attribut:[68,131,134,209],auc:[65,68],audienc:116,augment:[32,33,35,142],author:200,autoencod:[29,30,31,138],automl:[9,112,113,167],avail:131,averag:[104,198],avoid:[120,183],axi:131,azur:[9,10,20,112,113,130],b:[87,192],babylonian:105,backend:79,background:142,backprop:94,bag:[58,150,173,176,181],balanc:188,bar:[19,22,208],base:[30,40,104,133,181],basebal:18,baselin:[55,56,191,221],basi:68,basic:[29,32,33,48,50,105,124,131,134,144,159,195,202,203,206,209,224,225],batch:[34,71,145,184],batch_siz:139,beehiv:13,begin:116,beginn:42,behind:64,bert:[150,160],best:[9,32,39,112,169],beta:142,better:[189,197],between:[24,63,65,78,86,92,131,176,193,206,209],beyond:[46,58,182,214],bi:63,bia:[73,176,183,215],bibliographi:[25,192,193],bidirect:[150,153],big:222,binai:63,binari:[46,193,197],binder:0,bird:[121,208],bit:[31,131],bitcoin:41,blend:63,block:163,bmi:24,boost:[58,63,64,65,177,178,179,181],bootstrap:[173,183],bound:142,boundari:[182,184,214],bow:150,boxplot:[24,95],bp:24,brain: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