{"id":5985,"date":"2025-10-30T05:31:31","date_gmt":"2025-10-30T05:31:31","guid":{"rendered":"https:\/\/omxwebsites.com\/gammatica\/?page_id=5985"},"modified":"2025-11-10T04:24:24","modified_gmt":"2025-11-10T04:24:24","slug":"python-for-ai-ml","status":"publish","type":"page","link":"https:\/\/omxwebsites.com\/gammatica\/python-for-ai-ml\/","title":{"rendered":"Python for AI &#038; ML"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"5985\" class=\"elementor elementor-5985\">\n\t\t\t\t<div class=\"elementor-element elementor-element-86004eb e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"86004eb\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-366994e4 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"366994e4\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-22eb2b65 elementor-widget elementor-widget-heading\" data-id=\"22eb2b65\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Python for AI &amp; ML<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5ffd9c25 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"5ffd9c25\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-745635b3 elementor-widget elementor-widget-image\" data-id=\"745635b3\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"540\" height=\"360\" src=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/360_F_505413960_r4BUfFKXvzMkSCrXUl0HnMK8Bszuq6y4.jpg\" class=\"attachment-large size-large wp-image-5862\" alt=\"\" srcset=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/360_F_505413960_r4BUfFKXvzMkSCrXUl0HnMK8Bszuq6y4.jpg 540w, https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/360_F_505413960_r4BUfFKXvzMkSCrXUl0HnMK8Bszuq6y4-300x200.jpg 300w\" sizes=\"(max-width: 540px) 100vw, 540px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-30b829eb e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"30b829eb\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-a5485a5 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"a5485a5\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-59ad7b5a elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"59ad7b5a\" data-element_type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-book-reader\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M352 96c0-53.02-42.98-96-96-96s-96 42.98-96 96 42.98 96 96 96 96-42.98 96-96zM233.59 241.1c-59.33-36.32-155.43-46.3-203.79-49.05C13.55 191.13 0 203.51 0 219.14v222.8c0 14.33 11.59 26.28 26.49 27.05 43.66 2.29 131.99 10.68 193.04 41.43 9.37 4.72 20.48-1.71 20.48-11.87V252.56c-.01-4.67-2.32-8.95-6.42-11.46zm248.61-49.05c-48.35 2.74-144.46 12.73-203.78 49.05-4.1 2.51-6.41 6.96-6.41 11.63v245.79c0 10.19 11.14 16.63 20.54 11.9 61.04-30.72 149.32-39.11 192.97-41.4 14.9-.78 26.49-12.73 26.49-27.06V219.14c-.01-15.63-13.56-28.01-29.81-27.09z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5f485047 elementor-widget elementor-widget-heading\" data-id=\"5f485047\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Learning Format<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-372f1556 elementor-widget elementor-widget-text-editor\" data-id=\"372f1556\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Live Online \/ Classroom<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3efa0a4f e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"3efa0a4f\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-537feac4 elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"537feac4\" data-element_type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-clock\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8zm0 448c-110.5 0-200-89.5-200-200S145.5 56 256 56s200 89.5 200 200-89.5 200-200 200zm61.8-104.4l-84.9-61.7c-3.1-2.3-4.9-5.9-4.9-9.7V116c0-6.6 5.4-12 12-12h32c6.6 0 12 5.4 12 12v141.7l66.8 48.6c5.4 3.9 6.5 11.4 2.6 16.8L334.6 349c-3.9 5.3-11.4 6.5-16.8 2.6z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2a8036b7 elementor-widget elementor-widget-heading\" data-id=\"2a8036b7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Total training duration<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e8bb77b elementor-widget elementor-widget-text-editor\" data-id=\"e8bb77b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>120 hrs<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a7de4f7 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"a7de4f7\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3235fcfc elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"3235fcfc\" data-element_type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-book-open\" viewBox=\"0 0 576 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M542.22 32.05c-54.8 3.11-163.72 14.43-230.96 55.59-4.64 2.84-7.27 7.89-7.27 13.17v363.87c0 11.55 12.63 18.85 23.28 13.49 69.18-34.82 169.23-44.32 218.7-46.92 16.89-.89 30.02-14.43 30.02-30.66V62.75c.01-17.71-15.35-31.74-33.77-30.7zM264.73 87.64C197.5 46.48 88.58 35.17 33.78 32.05 15.36 31.01 0 45.04 0 62.75V400.6c0 16.24 13.13 29.78 30.02 30.66 49.49 2.6 149.59 12.11 218.77 46.95 10.62 5.35 23.21-1.94 23.21-13.46V100.63c0-5.29-2.62-10.14-7.27-12.99z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-538a9d18 elementor-widget elementor-widget-heading\" data-id=\"538a9d18\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Syllabus<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68926ee6 elementor-widget elementor-widget-text-editor\" data-id=\"68926ee6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>12 weeks<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-55a4cb75 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"55a4cb75\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-362a34ed elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"362a34ed\" data-element_type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-award\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M97.12 362.63c-8.69-8.69-4.16-6.24-25.12-11.85-9.51-2.55-17.87-7.45-25.43-13.32L1.2 448.7c-4.39 10.77 3.81 22.47 15.43 22.03l52.69-2.01L105.56 507c8 8.44 22.04 5.81 26.43-4.96l52.05-127.62c-10.84 6.04-22.87 9.58-35.31 9.58-19.5 0-37.82-7.59-51.61-21.37zM382.8 448.7l-45.37-111.24c-7.56 5.88-15.92 10.77-25.43 13.32-21.07 5.64-16.45 3.18-25.12 11.85-13.79 13.78-32.12 21.37-51.62 21.37-12.44 0-24.47-3.55-35.31-9.58L252 502.04c4.39 10.77 18.44 13.4 26.43 4.96l36.25-38.28 52.69 2.01c11.62.44 19.82-11.27 15.43-22.03zM263 340c15.28-15.55 17.03-14.21 38.79-20.14 13.89-3.79 24.75-14.84 28.47-28.98 7.48-28.4 5.54-24.97 25.95-45.75 10.17-10.35 14.14-25.44 10.42-39.58-7.47-28.38-7.48-24.42 0-52.83 3.72-14.14-.25-29.23-10.42-39.58-20.41-20.78-18.47-17.36-25.95-45.75-3.72-14.14-14.58-25.19-28.47-28.98-27.88-7.61-24.52-5.62-44.95-26.41-10.17-10.35-25-14.4-38.89-10.61-27.87 7.6-23.98 7.61-51.9 0-13.89-3.79-28.72.25-38.89 10.61-20.41 20.78-17.05 18.8-44.94 26.41-13.89 3.79-24.75 14.84-28.47 28.98-7.47 28.39-5.54 24.97-25.95 45.75-10.17 10.35-14.15 25.44-10.42 39.58 7.47 28.36 7.48 24.4 0 52.82-3.72 14.14.25 29.23 10.42 39.59 20.41 20.78 18.47 17.35 25.95 45.75 3.72 14.14 14.58 25.19 28.47 28.98C104.6 325.96 106.27 325 121 340c13.23 13.47 33.84 15.88 49.74 5.82a39.676 39.676 0 0 1 42.53 0c15.89 10.06 36.5 7.65 49.73-5.82zM97.66 175.96c0-53.03 42.24-96.02 94.34-96.02s94.34 42.99 94.34 96.02-42.24 96.02-94.34 96.02-94.34-42.99-94.34-96.02z\"><\/path><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5ab8a68f elementor-widget elementor-widget-heading\" data-id=\"5ab8a68f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\"> Certification<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7049ca12 elementor-widget elementor-widget-text-editor\" data-id=\"7049ca12\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tYes\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2837b751 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"2837b751\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-70979a5e elementor-widget elementor-widget-heading\" data-id=\"70979a5e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Python for AI &amp; ML<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7fda1f27 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"7fda1f27\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-54cb7494 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"54cb7494\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6b7ce27c elementor-widget elementor-widget-text-editor\" data-id=\"6b7ce27c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><strong data-start=\"0\" data-end=\"22\" data-is-only-node=\"\">Python for AI &amp; ML<\/strong> is widely used because of its simplicity and powerful ecosystem of libraries. It provides tools like <strong data-start=\"124\" data-end=\"133\">NumPy<\/strong>, <strong data-start=\"135\" data-end=\"145\">Pandas<\/strong>, <strong data-start=\"147\" data-end=\"163\">Scikit-learn<\/strong>, <strong data-start=\"165\" data-end=\"179\">TensorFlow<\/strong>, and <strong data-start=\"185\" data-end=\"196\">PyTorch<\/strong> for building intelligent systems. Python helps in data preprocessing, model training, evaluation, and deployment with ease. Its flexibility allows developers to experiment with algorithms for classification, prediction, and deep learning.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a07960e elementor-widget elementor-widget-button\" data-id=\"5a07960e\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/omxwebsites.com\/gammatica\/contact-us\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Enroll Now<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-283accdf e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"283accdf\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8834b42 elementor-widget elementor-widget-image\" data-id=\"8834b42\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"645\" height=\"387\" src=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/python-app-framework-1__1_-removebg-preview.png\" class=\"attachment-large size-large wp-image-5856\" alt=\"\" srcset=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/python-app-framework-1__1_-removebg-preview.png 645w, https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/python-app-framework-1__1_-removebg-preview-300x180.png 300w\" sizes=\"(max-width: 645px) 100vw, 645px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d915f0b e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"d915f0b\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-1968b82 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"1968b82\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1b3fcc9 elementor-widget elementor-widget-wpr-elementor-template\" data-id=\"1b3fcc9\" data-element_type=\"widget\" data-widget_type=\"wpr-elementor-template.default\">\n\t\t\t\t\t<style>.elementor-6004 .elementor-element.elementor-element-f21dd4d{--display:flex;--flex-direction:column;--container-widget-width:100%;--container-widget-height:initial;--container-widget-flex-grow:0;--container-widget-align-self:initial;--flex-wrap-mobile:wrap;}.elementor-6004 .elementor-element.elementor-element-8f41bdf{text-align:center;}.elementor-6004 .elementor-element.elementor-element-8f41bdf .elementor-heading-title{font-family:\"Jost\", 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.elementor-element.elementor-element-907df2b{--width:50%;}.elementor-6004 .elementor-element.elementor-element-e345457{--width:100%;}.elementor-6004 .elementor-element.elementor-element-4ec9d36{--width:50%;}}<\/style>\t\t<div data-elementor-type=\"section\" data-elementor-id=\"6004\" class=\"elementor elementor-6004\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f21dd4d e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"f21dd4d\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8f41bdf elementor-widget elementor-widget-heading\" data-id=\"8f41bdf\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Syllabus Summary<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cec094a e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"cec094a\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-907df2b e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"907df2b\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-e345457 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"e345457\" data-element_type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-76d92e3 elementor-widget elementor-widget-n-accordion\" data-id=\"76d92e3\" data-element_type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;all_collapsed&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1240\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1240\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 1 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1240\" class=\"elementor-element elementor-element-856a1ef e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"856a1ef\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1240\" class=\"elementor-element elementor-element-ab4b56b e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"ab4b56b\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8102c75 elementor-widget elementor-widget-text-editor\" data-id=\"8102c75\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"2235\" data-end=\"2278\">Python recap for ML (10 hours)<\/h3><p data-start=\"2279\" data-end=\"2375\"><strong data-start=\"2279\" data-end=\"2294\">Objectives:<\/strong>\u00a0Bring everyone to a consistent Python\/ML environment; basic numerical computing.<\/p><ul data-start=\"2376\" data-end=\"2815\"><li data-start=\"2376\" data-end=\"2481\"><p data-start=\"2378\" data-end=\"2481\">Topics: Python refresher (functions, OOP basics), virtual environments, Jupyter\/Colab, pip, Git basics.<\/p><\/li><li data-start=\"2482\" data-end=\"2572\"><p data-start=\"2484\" data-end=\"2572\">Libraries: NumPy (arrays, broadcasting), Pandas (DataFrame ops, indexing, missing data).<\/p><\/li><li data-start=\"2573\" data-end=\"2665\"><p data-start=\"2575\" data-end=\"2665\">Lab (4 h): Data cleaning workflow \u2014 load CSV, explore, impute, scale, feature engineering.<\/p><\/li><li data-start=\"2666\" data-end=\"2755\"><p data-start=\"2668\" data-end=\"2755\">Assignment (2 h): Clean &amp; document a messy ML dataset (deliver cleaned CSV + notebook).<\/p><\/li><li data-start=\"2756\" data-end=\"2815\"><p data-start=\"2758\" data-end=\"2815\">Deliverable: Cleaned dataset + brief data quality report.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1241\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1241\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 2 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1241\" class=\"elementor-element elementor-element-9831a16 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"9831a16\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1241\" class=\"elementor-element elementor-element-3dc4424 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"3dc4424\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-da5e82f elementor-widget elementor-widget-text-editor\" data-id=\"da5e82f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"2817\" data-end=\"2866\">ML intro &amp; data splitting (10 hours)<\/h3><p data-start=\"2867\" data-end=\"2943\"><strong data-start=\"2867\" data-end=\"2882\">Objectives:<\/strong>\u00a0ML pipeline, supervised vs unsupervised, holdout strategies.<\/p><ul data-start=\"2944\" data-end=\"3287\"><li data-start=\"2944\" data-end=\"3042\"><p data-start=\"2946\" data-end=\"3042\">Topics: ML workflow, features\/labels, train\/validation\/test splits, cross-validation, pipelines.<\/p><\/li><li data-start=\"3043\" data-end=\"3134\"><p data-start=\"3045\" data-end=\"3134\">Lab (4 h): House price prediction (simple linear regression baseline using scikit-learn).<\/p><\/li><li data-start=\"3135\" data-end=\"3211\"><p data-start=\"3137\" data-end=\"3211\">Assignment (2 h): Build baseline model, report metrics and error analysis.<\/p><\/li><li data-start=\"3212\" data-end=\"3287\"><p data-start=\"3214\" data-end=\"3287\">Deliverable: Notebook with train\/val\/test split, baseline model, metrics.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1242\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1242\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 3 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1242\" class=\"elementor-element elementor-element-35922ba e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"35922ba\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1242\" class=\"elementor-element elementor-element-e3785c1 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"e3785c1\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5a02b11 elementor-widget elementor-widget-text-editor\" data-id=\"5a02b11\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"3289\" data-end=\"3330\">Regression models (10 hours)<\/h3><p data-start=\"3331\" data-end=\"3408\"><strong data-start=\"3331\" data-end=\"3346\">Objectives:<\/strong>\u00a0Linear &amp; non-linear regression techniques and regularization.<\/p><ul data-start=\"3409\" data-end=\"3713\"><li data-start=\"3409\" data-end=\"3515\"><p data-start=\"3411\" data-end=\"3515\">Topics: Linear Regression, Polynomial features, Ridge, Lasso, feature selection, bias-variance tradeoff.<\/p><\/li><li data-start=\"3516\" data-end=\"3591\"><p data-start=\"3518\" data-end=\"3591\">Lab (4 h): Salary prediction project (feature engineering + Ridge\/Lasso).<\/p><\/li><li data-start=\"3592\" data-end=\"3664\"><p data-start=\"3594\" data-end=\"3664\">Assignment (2 h): Compare models, cross-validate, submit short report.<\/p><\/li><li data-start=\"3665\" data-end=\"3713\"><p data-start=\"3667\" data-end=\"3713\">Deliverable: Notebook + comparison plot\/table.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1243\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1243\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 4 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1243\" class=\"elementor-element elementor-element-b17d221 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"b17d221\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1243\" class=\"elementor-element elementor-element-e0021a1 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"e0021a1\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-04dedca elementor-widget elementor-widget-text-editor\" data-id=\"04dedca\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"3715\" data-end=\"3760\">Classification models (10 hours)<\/h3><p data-start=\"3761\" data-end=\"3818\"><strong data-start=\"3761\" data-end=\"3776\">Objectives:<\/strong>\u00a0Classification algorithms and evaluation.<\/p><ul data-start=\"3819\" data-end=\"4223\"><li data-start=\"3819\" data-end=\"3970\"><p data-start=\"3821\" data-end=\"3970\">Topics: Logistic Regression, k-NN, Decision Trees, Random Forests, ROC\/AUC, confusion matrix, class imbalance strategies (resampling, class weights).<\/p><\/li><li data-start=\"3971\" data-end=\"4022\"><p data-start=\"3973\" data-end=\"4022\">Lab (4 h): Iris classifier + multiclass handling.<\/p><\/li><li data-start=\"4023\" data-end=\"4093\"><p data-start=\"4025\" data-end=\"4093\">Mock Interview 1 (2 h): Short technical + practical coding question.<\/p><\/li><li data-start=\"4094\" data-end=\"4171\"><p data-start=\"4096\" data-end=\"4171\">Assignment (2 h): Build &amp; evaluate a classifier with hyperparameter tuning.<\/p><\/li><li data-start=\"4172\" data-end=\"4223\"><p data-start=\"4174\" data-end=\"4223\">Deliverable: Notebook + model evaluation summary.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1244\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"5\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1244\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 5 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1244\" class=\"elementor-element elementor-element-4eeeab5 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"4eeeab5\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1244\" class=\"elementor-element elementor-element-c5c5691 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"c5c5691\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3c20b56 elementor-widget elementor-widget-text-editor\" data-id=\"3c20b56\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"4225\" data-end=\"4282\">Clustering &amp; unsupervised methods (10 hours)<\/h3><p data-start=\"4283\" data-end=\"4350\"><strong data-start=\"4283\" data-end=\"4298\">Objectives:<\/strong>\u00a0Unsupervised learning methods and when to use them.<\/p><ul data-start=\"4351\" data-end=\"4653\"><li data-start=\"4351\" data-end=\"4436\"><p data-start=\"4353\" data-end=\"4436\">Topics: K-means, Hierarchical clustering, DBSCAN, PCA for dimensionality reduction.<\/p><\/li><li data-start=\"4437\" data-end=\"4508\"><p data-start=\"4439\" data-end=\"4508\">Lab (4 h): Customer segmentation (cluster analysis + interpretation).<\/p><\/li><li data-start=\"4509\" data-end=\"4578\"><p data-start=\"4511\" data-end=\"4578\">Assignment (2 h): Create clusters, visualize, and profile segments.<\/p><\/li><li data-start=\"4579\" data-end=\"4653\"><p data-start=\"4581\" data-end=\"4653\">Deliverable: Notebook with cluster visualizations and business insights.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-1245\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"6\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-1245\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 6 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1245\" class=\"elementor-element elementor-element-d8a31c4 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"d8a31c4\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-1245\" class=\"elementor-element elementor-element-c10b204 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"c10b204\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3c4f684 elementor-widget elementor-widget-text-editor\" data-id=\"3c4f684\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"4655\" data-end=\"4715\">Model evaluation &amp; deployment basics (10 hours)<\/h3><p data-start=\"4716\" data-end=\"4786\"><strong data-start=\"4716\" data-end=\"4731\">Objectives:<\/strong>\u00a0Robust evaluation and basic model deployment concepts.<\/p><ul data-start=\"4787\" data-end=\"5255\"><li data-start=\"4787\" data-end=\"5002\"><p data-start=\"4789\" data-end=\"5002\">Topics: Cross-validation in depth, stratified sampling, metrics for various tasks, model selection, overfitting remedies. Intro to model serialization (pickle, joblib) and serving options (Flask\/Streamlit basics).<\/p><\/li><li data-start=\"5003\" data-end=\"5104\"><p data-start=\"5005\" data-end=\"5104\">Lab (4 h): Fraud detection dataset \u2014 work on imbalanced classification, precision\/recall tradeoffs.<\/p><\/li><li data-start=\"5105\" data-end=\"5189\"><p data-start=\"5107\" data-end=\"5189\">Assignment (2 h): Build best performing model and export it; write inference demo.<\/p><\/li><li data-start=\"5190\" data-end=\"5255\"><p data-start=\"5192\" data-end=\"5255\">Deliverable: Exported model file + inference notebook\/web demo.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4ec9d36 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"4ec9d36\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-48405a7 elementor-widget elementor-widget-n-accordion\" data-id=\"48405a7\" data-element_type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;all_collapsed&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7570\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7570\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 7 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7570\" class=\"elementor-element elementor-element-ea2aedd e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"ea2aedd\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7570\" class=\"elementor-element elementor-element-82626c4 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"82626c4\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-98b9ed2 elementor-widget elementor-widget-text-editor\" data-id=\"98b9ed2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"5257\" data-end=\"5303\">Neural networks basics (10 hours)<\/h3><p data-start=\"5304\" data-end=\"5370\"><strong data-start=\"5304\" data-end=\"5319\">Objectives:<\/strong>\u00a0Introduce deep learning concepts and build an ANN.<\/p><ul data-start=\"5371\" data-end=\"5836\"><li data-start=\"5371\" data-end=\"5542\"><p data-start=\"5373\" data-end=\"5542\">Topics: Perceptron, multilayer perceptron, activation functions, forward\/backprop intuition, loss functions, optimizers (SGD\/Adam), regularization (dropout, batch norm).<\/p><\/li><li data-start=\"5543\" data-end=\"5618\"><p data-start=\"5545\" data-end=\"5618\">Framework focus: TensorFlow (Keras Sequential) or PyTorch (basic Module).<\/p><\/li><li data-start=\"5619\" data-end=\"5696\"><p data-start=\"5621\" data-end=\"5696\">Lab (4 h): Build a simple ANN for a tabular classification\/regression task.<\/p><\/li><li data-start=\"5697\" data-end=\"5788\"><p data-start=\"5699\" data-end=\"5788\">Assignment (2 h): Implement network training loop, log metrics, and plot learning curves.<\/p><\/li><li data-start=\"5789\" data-end=\"5836\"><p data-start=\"5791\" data-end=\"5836\">Deliverable: Training notebook + saved model.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7571\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7571\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 8 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7571\" class=\"elementor-element elementor-element-4fb518e e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"4fb518e\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7571\" class=\"elementor-element elementor-element-c968ce3 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"c968ce3\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3317156 elementor-widget elementor-widget-text-editor\" data-id=\"3317156\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"5838\" data-end=\"5872\">CNN basics (10 hours)<\/h3><p data-start=\"5873\" data-end=\"5935\"><strong data-start=\"5873\" data-end=\"5888\">Objectives:<\/strong>\u00a0Convolutional Neural Networks for image tasks.<\/p><ul data-start=\"5936\" data-end=\"6330\"><li data-start=\"5936\" data-end=\"6038\"><p data-start=\"5938\" data-end=\"6038\">Topics: Convolutions, pooling, architectures (LeNet\/VGG concepts), data augmentation, training tips.<\/p><\/li><li data-start=\"6039\" data-end=\"6131\"><p data-start=\"6041\" data-end=\"6131\">Lab (4 h): MNIST digit classifier (or small CIFAR subset) with augmentation and callbacks.<\/p><\/li><li data-start=\"6132\" data-end=\"6193\"><p data-start=\"6134\" data-end=\"6193\">Mock Interview 2 (2 h): Model design + debugging challenge.<\/p><\/li><li data-start=\"6194\" data-end=\"6289\"><p data-start=\"6196\" data-end=\"6289\">Assignment (2 h): Improve accuracy via augmentation\/regularization; produce confusion matrix.<\/p><\/li><li data-start=\"6290\" data-end=\"6330\"><p data-start=\"6292\" data-end=\"6330\">Deliverable: Notebook + model metrics.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7572\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7572\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 9 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7572\" class=\"elementor-element elementor-element-520f0d5 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"520f0d5\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7572\" class=\"elementor-element elementor-element-754cca2 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"754cca2\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a679f50 elementor-widget elementor-widget-text-editor\" data-id=\"a679f50\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"6332\" data-end=\"6396\">NLP preprocessing &amp; classic NLP pipeline (10 hours)<\/h3><p data-start=\"6397\" data-end=\"6481\"><strong data-start=\"6397\" data-end=\"6412\">Objectives:<\/strong>\u00a0Text preprocessing and classical approaches for text classification.<\/p><ul data-start=\"6482\" data-end=\"6797\"><li data-start=\"6482\" data-end=\"6597\"><p data-start=\"6484\" data-end=\"6597\">Topics: Tokenization, stemming\/lemmatization (NLTK &amp; spaCy), stopwords, TF-IDF, bag-of-words, text normalization.<\/p><\/li><li data-start=\"6598\" data-end=\"6675\"><p data-start=\"6600\" data-end=\"6675\">Lab (4 h): News classification (preprocess, vectorize, train a classifier).<\/p><\/li><li data-start=\"6676\" data-end=\"6742\"><p data-start=\"6678\" data-end=\"6742\">Assignment (2 h): End-to-end text pipeline and model evaluation.<\/p><\/li><li data-start=\"6743\" data-end=\"6797\"><p data-start=\"6745\" data-end=\"6797\">Deliverable: Notebook + preprocessing pipeline code.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7573\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7573\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 10 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7573\" class=\"elementor-element elementor-element-54174d0 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"54174d0\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7573\" class=\"elementor-element elementor-element-5792994 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"5792994\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f13ea1e elementor-widget elementor-widget-text-editor\" data-id=\"f13ea1e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"6799\" data-end=\"6857\">Word embeddings &amp; sequence models (10 hours)<\/h3><p data-start=\"6858\" data-end=\"6930\"><strong data-start=\"6858\" data-end=\"6873\">Objectives:<\/strong>\u00a0Dense representations and basic sequence models for NLP.<\/p><ul data-start=\"6931\" data-end=\"7302\"><li data-start=\"6931\" data-end=\"7092\"><p data-start=\"6933\" data-end=\"7092\">Topics: Word2Vec \/ GloVe \/ pretrained embeddings, Embedding layers, simple RNN\/LSTM\/GRU intuition, classification with embeddings; sentiment analysis pipeline.<\/p><\/li><li data-start=\"7093\" data-end=\"7172\"><p data-start=\"7095\" data-end=\"7172\">Lab (4 h): Sentiment analysis using pretrained embeddings or embedding layer.<\/p><\/li><li data-start=\"7173\" data-end=\"7254\"><p data-start=\"7175\" data-end=\"7254\">Assignment (2 h): Build &amp; compare embedding strategies (pretrained vs learned).<\/p><\/li><li data-start=\"7255\" data-end=\"7302\"><p data-start=\"7257\" data-end=\"7302\">Deliverable: Notebook + comparative analysis.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7574\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"5\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7574\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 11 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7574\" class=\"elementor-element elementor-element-cd37a2d e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"cd37a2d\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7574\" class=\"elementor-element elementor-element-91fa955 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"91fa955\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8ce979a elementor-widget elementor-widget-text-editor\" data-id=\"8ce979a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"7304\" data-end=\"7364\">Transfer learning &amp; advanced topics (10 hours)<\/h3><p data-start=\"7365\" data-end=\"7464\"><strong data-start=\"7365\" data-end=\"7380\">Objectives:<\/strong>\u00a0Leverage pretrained models, introduction to fine-tuning and lightweight deployment.<\/p><ul data-start=\"7465\" data-end=\"7933\"><li data-start=\"7465\" data-end=\"7663\"><p data-start=\"7467\" data-end=\"7663\">Topics: Transfer learning for vision (feature extraction vs fine-tuning), model checkpoints, saving\/loading TF\/PyTorch models, lightweight inference (TensorFlow Lite mention), ethics &amp; bias in ML.<\/p><\/li><li data-start=\"7664\" data-end=\"7819\"><p data-start=\"7666\" data-end=\"7819\">Lab (4 h): Mini chatbot (intent classification + simple rule-based responses) OR image transfer learning (fine-tune a pretrained model on small dataset).<\/p><\/li><li data-start=\"7820\" data-end=\"7889\"><p data-start=\"7822\" data-end=\"7889\">Assignment (2 h): Small project showing transfer learning benefits.<\/p><\/li><li data-start=\"7890\" data-end=\"7933\"><p data-start=\"7892\" data-end=\"7933\">Deliverable: Notebook + fine-tuned model.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-7575\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"6\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-7575\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><div class=\"e-n-accordion-item-title-text\"> Week 12 <\/div><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7575\" class=\"elementor-element elementor-element-9b36710 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"9b36710\" data-element_type=\"container\">\n\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-7575\" class=\"elementor-element elementor-element-85e7a72 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"85e7a72\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-67af02f elementor-widget elementor-widget-text-editor\" data-id=\"67af02f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h3 data-start=\"7935\" data-end=\"7985\">Capstone &amp; interview prep (10 hours)<\/h3><p data-start=\"7986\" data-end=\"8064\"><strong data-start=\"7986\" data-end=\"8001\">Objectives:<\/strong>\u00a0Integrate everything in a final project; final mock interview.<\/p><ul data-start=\"8065\" data-end=\"8543\"><li data-start=\"8065\" data-end=\"8165\"><p data-start=\"8067\" data-end=\"8165\">Topics: Project presentations, code review, reproducibility, model cards, deployment demo (basic).<\/p><\/li><li data-start=\"8166\" data-end=\"8397\"><p data-start=\"8168\" data-end=\"8208\">Capstone Projects (choose one or two):<\/p><ul data-start=\"8211\" data-end=\"8397\"><li data-start=\"8211\" data-end=\"8278\"><p data-start=\"8213\" data-end=\"8278\">Sentiment Analysis (end-to-end: data \u2192 model \u2192 deployment demo)<\/p><\/li><li data-start=\"8281\" data-end=\"8338\"><p data-start=\"8283\" data-end=\"8338\">Image Classifier (transfer learning, deployment demo)<\/p><\/li><li data-start=\"8341\" data-end=\"8397\"><p data-start=\"8343\" data-end=\"8397\">Sales Prediction (time series + regression pipeline)<\/p><\/li><\/ul><\/li><li data-start=\"8398\" data-end=\"8476\"><p data-start=\"8400\" data-end=\"8476\">Mock Interview 3 (2 h): Full technical + system design + HR style questions.<\/p><\/li><li data-start=\"8477\" data-end=\"8543\"><p data-start=\"8479\" data-end=\"8543\">Deliverable: Capstone project repo, presentation, README + demo.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<span class=\"wpr-template-edit-btn\" data-permalink=\"https:\/\/omxwebsites.com\/gammatica\/?elementor_library=python-for-ai-ml-syllabus\">Edit Template<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fa025ed elementor-widget elementor-widget-heading\" data-id=\"fa025ed\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Course Summary<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7f0a3ad6 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"7f0a3ad6\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-12eae482 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"12eae482\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-57939a6 elementor-position-left elementor-view-default elementor-mobile-position-top elementor-widget elementor-widget-icon-box\" data-id=\"57939a6\" data-element_type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<i aria-hidden=\"true\" class=\"icon icon-bar-chart\"><\/i>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tEligibility\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tTech &amp; Non-Tech Working professional, Freshers, Graduate from any domain.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6919634 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"6919634\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1e19f785 elementor-position-left elementor-view-default elementor-mobile-position-top elementor-widget elementor-widget-icon-box\" data-id=\"1e19f785\" data-element_type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-lightbulb\" viewBox=\"0 0 352 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M176 80c-52.94 0-96 43.06-96 96 0 8.84 7.16 16 16 16s16-7.16 16-16c0-35.3 28.72-64 64-64 8.84 0 16-7.16 16-16s-7.16-16-16-16zM96.06 459.17c0 3.15.93 6.22 2.68 8.84l24.51 36.84c2.97 4.46 7.97 7.14 13.32 7.14h78.85c5.36 0 10.36-2.68 13.32-7.14l24.51-36.84c1.74-2.62 2.67-5.7 2.68-8.84l.05-43.18H96.02l.04 43.18zM176 0C73.72 0 0 82.97 0 176c0 44.37 16.45 84.85 43.56 115.78 16.64 18.99 42.74 58.8 52.42 92.16v.06h48v-.12c-.01-4.77-.72-9.51-2.15-14.07-5.59-17.81-22.82-64.77-62.17-109.67-20.54-23.43-31.52-53.15-31.61-84.14-.2-73.64 59.67-128 127.95-128 70.58 0 128 57.42 128 128 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elementor-element-6e273896 elementor-position-left elementor-view-default elementor-mobile-position-top elementor-widget elementor-widget-icon-box\" data-id=\"6e273896\" data-element_type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-user\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm89.6 32h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-74.2-60.2-134.4-134.4-134.4z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tInstructor\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tExperts and trainer for top-tech companies.\n\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3e3ddbc8 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"3e3ddbc8\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-37bd497f e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"37bd497f\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-203d870e elementor-position-left elementor-view-default elementor-mobile-position-top elementor-widget elementor-widget-icon-box\" data-id=\"203d870e\" data-element_type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-award\" viewBox=\"0 0 384 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M97.12 362.63c-8.69-8.69-4.16-6.24-25.12-11.85-9.51-2.55-17.87-7.45-25.43-13.32L1.2 448.7c-4.39 10.77 3.81 22.47 15.43 22.03l52.69-2.01L105.56 507c8 8.44 22.04 5.81 26.43-4.96l52.05-127.62c-10.84 6.04-22.87 9.58-35.31 9.58-19.5 0-37.82-7.59-51.61-21.37zM382.8 448.7l-45.37-111.24c-7.56 5.88-15.92 10.77-25.43 13.32-21.07 5.64-16.45 3.18-25.12 11.85-13.79 13.78-32.12 21.37-51.62 21.37-12.44 0-24.47-3.55-35.31-9.58L252 502.04c4.39 10.77 18.44 13.4 26.43 4.96l36.25-38.28 52.69 2.01c11.62.44 19.82-11.27 15.43-22.03zM263 340c15.28-15.55 17.03-14.21 38.79-20.14 13.89-3.79 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class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tCertification\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\t10+ ISO Globally recognized certified\n\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-2daa7827 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"2daa7827\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4eb1c281 elementor-position-left elementor-view-default elementor-mobile-position-top elementor-widget elementor-widget-icon-box\" data-id=\"4eb1c281\" data-element_type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-mobile-alt\" viewBox=\"0 0 320 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M272 0H48C21.5 0 0 21.5 0 48v416c0 26.5 21.5 48 48 48h224c26.5 0 48-21.5 48-48V48c0-26.5-21.5-48-48-48zM160 480c-17.7 0-32-14.3-32-32s14.3-32 32-32 32 14.3 32 32-14.3 32-32 32zm112-108c0 6.6-5.4 12-12 12H60c-6.6 0-12-5.4-12-12V60c0-6.6 5.4-12 12-12h200c6.6 0 12 5.4 12 12v312z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tMode of Learning\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\t100% Live Learning with experienced instructors and hands-on sessions.\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-141d02ba e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"141d02ba\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7abef77e elementor-position-left elementor-view-default elementor-mobile-position-top elementor-widget elementor-widget-icon-box\" data-id=\"7abef77e\" data-element_type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\t\t\t<div class=\"elementor-icon-box-icon\">\n\t\t\t\t<span  class=\"elementor-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-book\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M448 360V24c0-13.3-10.7-24-24-24H96C43 0 0 43 0 96v320c0 53 43 96 96 96h328c13.3 0 24-10.7 24-24v-16c0-7.5-3.5-14.3-8.9-18.7-4.2-15.4-4.2-59.3 0-74.7 5.4-4.3 8.9-11.1 8.9-18.6zM128 134c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm0 64c0-3.3 2.7-6 6-6h212c3.3 0 6 2.7 6 6v20c0 3.3-2.7 6-6 6H134c-3.3 0-6-2.7-6-6v-20zm253.4 250H96c-17.7 0-32-14.3-32-32 0-17.6 14.4-32 32-32h285.4c-1.9 17.1-1.9 46.9 0 64z\"><\/path><\/svg>\t\t\t\t<\/span>\n\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h3 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tReal time projects\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tGet practical experience with real-world projects for a career in analytics.\n\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-19435b86 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"19435b86\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5b6f6379 elementor-widget elementor-widget-heading\" data-id=\"5b6f6379\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Certification<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-658609bd e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"658609bd\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-4969c394 e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"4969c394\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-38a0f488 elementor-widget elementor-widget-image\" data-id=\"38a0f488\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"486\" height=\"349\" src=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2.png\" class=\"attachment-large size-large wp-image-4521\" alt=\"\" srcset=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2.png 486w, https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2-300x215.png 300w\" sizes=\"(max-width: 486px) 100vw, 486px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3e432e4e e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"3e432e4e\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6b4c71cc elementor-widget elementor-widget-image\" data-id=\"6b4c71cc\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"486\" height=\"349\" src=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2.png\" class=\"attachment-large size-large wp-image-4521\" alt=\"\" srcset=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2.png 486w, https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2-300x215.png 300w\" sizes=\"(max-width: 486px) 100vw, 486px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3815386f e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-child\" data-id=\"3815386f\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7f8b8eda elementor-widget elementor-widget-image\" data-id=\"7f8b8eda\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"486\" height=\"349\" src=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2.png\" class=\"attachment-large size-large wp-image-4521\" alt=\"\" srcset=\"https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2.png 486w, https:\/\/omxwebsites.com\/gammatica\/wp-content\/uploads\/2025\/10\/certificate2-300x215.png 300w\" sizes=\"(max-width: 486px) 100vw, 486px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Python for AI &amp; ML Learning Format Live Online \/ Classroom Total training duration 120 hrs Syllabus 12 weeks Certification Yes Python for AI &amp; ML Python for AI &amp; ML is widely used because of its simplicity and powerful ecosystem of libraries. It provides tools like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch for building intelligent systems. Python helps in data preprocessing, model training, evaluation, and deployment with ease. Its flexibility allows developers to experiment with algorithms for classification, prediction, and deep learning. Enroll Now Syllabus Summary Week 1 Python recap for ML (10 hours) Objectives:\u00a0Bring everyone to a consistent Python\/ML environment; basic numerical computing. Topics: Python refresher (functions, OOP basics), virtual environments, Jupyter\/Colab, pip, Git basics. Libraries: NumPy (arrays, broadcasting), Pandas (DataFrame ops, indexing, missing data). Lab (4 h): Data cleaning workflow \u2014 load CSV, explore, impute, scale, feature engineering. Assignment (2 h): Clean &amp; document a messy ML dataset (deliver cleaned CSV + notebook). Deliverable: Cleaned dataset + brief data quality report. Week 2 ML intro &amp; data splitting (10 hours) Objectives:\u00a0ML pipeline, supervised vs unsupervised, holdout strategies. Topics: ML workflow, features\/labels, train\/validation\/test splits, cross-validation, pipelines. Lab (4 h): House price prediction (simple linear regression baseline using scikit-learn). Assignment (2 h): Build baseline model, report metrics and error analysis. Deliverable: Notebook with train\/val\/test split, baseline model, metrics. Week 3 Regression models (10 hours) Objectives:\u00a0Linear &amp; non-linear regression techniques and regularization. Topics: Linear Regression, Polynomial features, Ridge, Lasso, feature selection, bias-variance tradeoff. Lab (4 h): Salary prediction project (feature engineering + Ridge\/Lasso). Assignment (2 h): Compare models, cross-validate, submit short report. Deliverable: Notebook + comparison plot\/table. Week 4 Classification models (10 hours) Objectives:\u00a0Classification algorithms and evaluation. Topics: Logistic Regression, k-NN, Decision Trees, Random Forests, ROC\/AUC, confusion matrix, class imbalance strategies (resampling, class weights). Lab (4 h): Iris classifier + multiclass handling. Mock Interview 1 (2 h): Short technical + practical coding question. Assignment (2 h): Build &amp; evaluate a classifier with hyperparameter tuning. Deliverable: Notebook + model evaluation summary. Week 5 Clustering &amp; unsupervised methods (10 hours) Objectives:\u00a0Unsupervised learning methods and when to use them. Topics: K-means, Hierarchical clustering, DBSCAN, PCA for dimensionality reduction. Lab (4 h): Customer segmentation (cluster analysis + interpretation). Assignment (2 h): Create clusters, visualize, and profile segments. Deliverable: Notebook with cluster visualizations and business insights. Week 6 Model evaluation &amp; deployment basics (10 hours) Objectives:\u00a0Robust evaluation and basic model deployment concepts. Topics: Cross-validation in depth, stratified sampling, metrics for various tasks, model selection, overfitting remedies. Intro to model serialization (pickle, joblib) and serving options (Flask\/Streamlit basics). Lab (4 h): Fraud detection dataset \u2014 work on imbalanced classification, precision\/recall tradeoffs. Assignment (2 h): Build best performing model and export it; write inference demo. Deliverable: Exported model file + inference notebook\/web demo. Week 7 Neural networks basics (10 hours) Objectives:\u00a0Introduce deep learning concepts and build an ANN. Topics: Perceptron, multilayer perceptron, activation functions, forward\/backprop intuition, loss functions, optimizers (SGD\/Adam), regularization (dropout, batch norm). Framework focus: TensorFlow (Keras Sequential) or PyTorch (basic Module). Lab (4 h): Build a simple ANN for a tabular classification\/regression task. Assignment (2 h): Implement network training loop, log metrics, and plot learning curves. Deliverable: Training notebook + saved model. Week 8 CNN basics (10 hours) Objectives:\u00a0Convolutional Neural Networks for image tasks. Topics: Convolutions, pooling, architectures (LeNet\/VGG concepts), data augmentation, training tips. Lab (4 h): MNIST digit classifier (or small CIFAR subset) with augmentation and callbacks. Mock Interview 2 (2 h): Model design + debugging challenge. Assignment (2 h): Improve accuracy via augmentation\/regularization; produce confusion matrix. Deliverable: Notebook + model metrics. Week 9 NLP preprocessing &amp; classic NLP pipeline (10 hours) Objectives:\u00a0Text preprocessing and classical approaches for text classification. Topics: Tokenization, stemming\/lemmatization (NLTK &amp; spaCy), stopwords, TF-IDF, bag-of-words, text normalization. Lab (4 h): News classification (preprocess, vectorize, train a classifier). Assignment (2 h): End-to-end text pipeline and model evaluation. Deliverable: Notebook + preprocessing pipeline code. Week 10 Word embeddings &amp; sequence models (10 hours) Objectives:\u00a0Dense representations and basic sequence models for NLP. Topics: Word2Vec \/ GloVe \/ pretrained embeddings, Embedding layers, simple RNN\/LSTM\/GRU intuition, classification with embeddings; sentiment analysis pipeline. Lab (4 h): Sentiment analysis using pretrained embeddings or embedding layer. Assignment (2 h): Build &amp; compare embedding strategies (pretrained vs learned). Deliverable: Notebook + comparative analysis. Week 11 Transfer learning &amp; advanced topics (10 hours) Objectives:\u00a0Leverage pretrained models, introduction to fine-tuning and lightweight deployment. Topics: Transfer learning for vision (feature extraction vs fine-tuning), model checkpoints, saving\/loading TF\/PyTorch models, lightweight inference (TensorFlow Lite mention), ethics &amp; bias in ML. Lab (4 h): Mini chatbot (intent classification + simple rule-based responses) OR image transfer learning (fine-tune a pretrained model on small dataset). Assignment (2 h): Small project showing transfer learning benefits. Deliverable: Notebook + fine-tuned model. Week 12 Capstone &amp; interview prep (10 hours) Objectives:\u00a0Integrate everything in a final project; final mock interview. Topics: Project presentations, code review, reproducibility, model cards, deployment demo (basic). Capstone Projects (choose one or two): Sentiment Analysis (end-to-end: data \u2192 model \u2192 deployment demo) Image Classifier (transfer learning, deployment demo) Sales Prediction (time series + regression pipeline) Mock Interview 3 (2 h): Full technical + system design + HR style questions. Deliverable: Capstone project repo, presentation, README + demo. Edit Template Course Summary Eligibility Tech &amp; Non-Tech Working professional, Freshers, Graduate from any domain. Live Doubt Solving Get your queries solved with daily dedicated doubts solving sessions. Instructor Experts and trainer for top-tech companies. Certification 10+ ISO Globally recognized certified Mode of Learning 100% Live Learning with experienced instructors and hands-on sessions. Real time projects Get practical experience with real-world projects for a career in analytics. Certification<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_canvas","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-5985","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/pages\/5985","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/comments?post=5985"}],"version-history":[{"count":18,"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/pages\/5985\/revisions"}],"predecessor-version":[{"id":7995,"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/pages\/5985\/revisions\/7995"}],"wp:attachment":[{"href":"https:\/\/omxwebsites.com\/gammatica\/wp-json\/wp\/v2\/media?parent=5985"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}