Curriculum
- 8 Sections
- 56 Lessons
- 52 Weeks
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- Course Overview/*! CSS Used from: Embedded */ *, ::after, ::before { box-sizing: border-box; border-width: 0; border-style: solid; border-color: #e5e7eb; } ::after, ::before { --tw-content: ''; } h2 { font-size: inherit; font-weight: inherit; } a { color: inherit; text-decoration: inherit; } h2, p { margin: 0; } :disabled { cursor: default; } *, ::before, ::after { --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-scroll-snap-strictness: proximity; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgb(59 130 246 / 0.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; } .mx-auto { margin-left: auto; margin-right: auto; } .mb-2 { margin-bottom: 0.5rem; } .mb-4 { margin-bottom: 1rem; } .mb-6 { margin-bottom: 1.5rem; } .mr-2 { margin-right: 0.5rem; } .max-w-screen-sm { max-width: 640px; } .max-w-screen-xl { max-width: 1280px; } .rounded-lg { border-radius: 0.5rem; } .bg-primary-700 { --tw-bg-opacity: 1; background-color: rgb(29 78 216 / var(--tw-bg-opacity)); } .bg-white { --tw-bg-opacity: 1; background-color: rgb(255 255 255 / var(--tw-bg-opacity)); } .px-4 { padding-left: 1rem; padding-right: 1rem; } .px-5 { padding-left: 1.25rem; padding-right: 1.25rem; } .py-2.5 { padding-top: 0.625rem; padding-bottom: 0.625rem; } .py-8 { padding-top: 2rem; padding-bottom: 2rem; } .text-center { text-align: center; } .text-4xl { font-size: 3rem; line-height: 2.5rem; } .text-sm { font-size: 0.875rem; line-height: 1.25rem; } .font-extrabold { font-weight: 800; } .font-light { font-weight: 300; } .font-medium { font-weight: 500; } .leading-tight { line-height: 1.25; } .tracking-tight { letter-spacing: -0.025em; } .text-gray-500 { --tw-text-opacity: 1; color: rgb(107 114 128 / var(--tw-text-opacity)); } .text-gray-900 { --tw-text-opacity: 1; color: rgb(17 24 39 / var(--tw-text-opacity)); } .text-white { --tw-text-opacity: 1; color: rgb(255 255 255 / var(--tw-text-opacity)); } .hover\:bg-primary-800:hover { --tw-bg-opacity: 1; background-color: rgb(30 64 175 / var(--tw-bg-opacity)); } .focus\:outline-none:focus { outline: 2px solid transparent; outline-offset: 2px; } .focus\:ring-4:focus { --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(4px + var(--tw-ring-offset-width)) var(--tw-ring-color); box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); } .focus\:ring-primary-300:focus { --tw-ring-opacity: 1; --tw-ring-color: rgb(147 197 253 / var(--tw-ring-opacity)); } @media (min-width: 640px) { .sm\:py-16 { padding-top: 4rem; padding-bottom: 4rem; } } @media (min-width: 768px) { .md\:text-lg { font-size: 1.5rem; line-height: 1.75rem; } } @media (min-width: 1024px) { .lg\:px-6 { padding-left: 1.5rem; padding-right: 1.5rem; } } .imgdata { width: 35% } @media (max-width: 767px) { .imgdata { width: 40% } } .course-payment { display: none !important; } .thim-course-landing-button { display: none !important; }0
- Introduction to Machine Learning4
- Learning with Regression and Trees5
- Learning with Classification and clustering13
- 4.1Bayes Theorem and Naive Bayes Classifier11 Minutes
- 4.2Bayesian Belief Network9 Minutes
- 4.3Markov Models8 Minutes
- 4.4Hidden Markov Model11 Minutes
- 4.5Support Vector Machine10 Minutes
- 4.6K mean Clustering Algorithm12 Minutes
- 4.7Apriori Algorithm with solved Example12 Minutes
- 4.8Agglomerative Algorithm with solved Example Part #113 Minutes
- 4.9Agglomerative Algorithm with solved Example Part #25 Minutes
- 4.10FP Tree Algorithm with Solved Example15 Minutes
- 4.11Hidden Markov model Basic Part 110 Minutes
- 4.12Hidden Markov model Basic Part 27 Minutes
- 4.13Hidden Markov model Basic Part 38 Minutes
- Dimensionality Reduction5
- Introduction to Neural Network13
- 6.1Introduction to ANN and structure of ANN6 Minutes
- 6.2Activation functions in ANN (Discrete and Continuous)4 Minutes
- 6.3Neural Network Architecture5 Minutes
- 6.4Linear Separability4 Minutes
- 6.5Mc-Culloch-Pitts Neural Model3 Minutes
- 6.6Hebbs Network/Hebbian Learning (with solved example)17 Minutes
- 6.7Winners-Takes-All5 Minutes
- 6.8Self Organizing Maps and KSOMs10 Minutes
- 6.9Linear Vector Quantization (LVQs)5 Minutes
- 6.10Derivation of Unipolar Continuous Function.7 Minutes
- 6.11Derivation of Bipolar Continuous Function11 Minutes
- 6.12Perceptron Learning (with solved example)11 Minutes
- 6.13Backpropagation Network (with solved example)19 Minutes
- Introduction to Optimization Techniques7
- Notes9
Key point in Machine Learning ( Basics of ML )
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