Course Features
- Lectures 124
- Quiz 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 6
- Assessments Yes
Curriculum
- 23 Sections
- 124 Lessons
- 24 Weeks
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- About the Course/*! 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
- Machine Learning [Module 1]:- Introduction to Machine Learning4
- Machine Learning [Module 2]:- Learning with Regression and Trees7
- Machine Learning [Module 3]:- Ensemble Learning6
- Machine Learning [Module 4]:- Learning with Classification2
- Machine Learning [Module 5]:- Learning with clustering7
- 6.1K mean Clustering Algorithm12 Minutes
- 6.2Apriori Algorithm with solved Example12 Minutes
- 6.3Agglomerative Algorithm with solved Example Part #113 Minutes
- 6.4Agglomerative Algorithm with solved Example Part #25 Minutes
- 6.5DBSCAN – Density Based Clustering8 Minutes
- 6.6Expectation Maximization Algorithm6 Minutes
- 6.7Clustering with Minimal Spanning Tree12 Minutes
- Machine Learning [Module 6]:- Dimensionality Reduction4
- Machine Learning [Module wise Imp Solution]6
- Big Data Analytics [Module 1]:- Introduction to Big Data and Hadoop3
- Big Data Analytics [Module 2]:- Hadoop HDFS and Map Reduce2
- Big Data Analytics [Module 3]:- NoSql5
- Big Data Analytics [Module 4]:- Mining Data Streams6
- Big Data Analytics [Module 5]:- Real Time Big Data Model4
- Big Data Analytics [Module 6]:- Data Analytics with R12
- 14.1Introduction – R Programming – #112 Minutes
- 14.2Introduction – R Programming – #216 Minutes
- 14.3Vectors in R Programming17 Minutes
- 14.4Objects in R Programming18 Minutes
- 14.5Interacting With Users12 Minutes
- 14.6Script in R13 Minutes
- 14.7Plot in R17 Minutes
- 14.8Exploring Dataset in R25 Minutes
- 14.9Function in R12 Minutes
- 14.10Visualization in R16 Minutes
- 14.11Module 6 Numerical IMP Solution27 Minutes
- 14.12R Programing Numerical Dec 202334 Minutes
- Big Data Analytics [Module wise Notes]6
- Big Data Analytics [Module wise IMP Solution]7
- Natural Language Processing [Module 1]:- Introduction to NLP5
- Natural Language Processing [Module 2]:- Word Level Analysis7
- Natural Language Processing [Module 3]:- Syntax analysis13
- 19.1POS Tagging10 Minutes
- 19.2Syntax Analysis3 Minutes
- 19.3Tag-set for English12 Minutes
- 19.4Rule Based POS6 Minutes
- 19.5Stochastic Part of Speech Tagging8 Minutes
- 19.6Transformation Based Tagging6 Minutes
- 19.7Multiple Tags ,Word and Unknown Words4 Minutes
- 19.8Basic Concept of Grammar and Parse Tree9 Minutes
- 19.9Parsing in NLP6 Minutes
- 19.10Hidden Markov Model Part 110 Minutes
- 19.11Hidden Markov Model Part 27 Minutes
- 19.12Viterbi Algorithm8 Minutes
- 19.13Syntax Analysis [Notes]
- Natural Language Processing [Module 4]:- Semantic Analysis8
- 20.1Introduction to Semantic Analysis13 Minutes
- 20.2Element of Semantic Analysis6 Minutes
- 20.3Attachment for Fragment of English (Phrases #1)9 Minutes
- 20.4Attachment for Fragment of English (Phrases #2)5 Minutes
- 20.5Attachment for Fragment of English (Phrases #3)4 Minutes
- 20.6WordNet8 Minutes
- 20.7Word Sense Disambiguation (WSD)9 Minutes
- 20.8Semantic Analysis [Notes]
- Natural Language Processing [Module 5]:- Pragmatic & Discourse Processing3
- Natural Language Processing [Module wise Imp Solution]6
- Natural Language Processing [Viva Questions]1