Sem 7 AIML [ Deep Learning – BDA – NLP ]
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About the Course
LMT - SEBI GRADE A 2025 - IT This Course is Only Available
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Big Data Analytics [Videos]
- M1:- Introduction to Big Data Analytics
- M1:-Introduction to Hadoop Part #1
- M1:-Introduction to Hadoop Part #2
- M2:-Introduction to MapReduce
- M2:-Map Reduce Word Count Problem PYQ Numericals
- M2:-Matrix Multiplication
- M3:-Introduction to No SQL Database
- M3:-Key-Value Stores
- M3:-Column Store Database
- M3:-Document Database
- M3:-Graph Database
- M4:-Data Stream Management System
- M4:-Sampling Techniques – Part 1
- M4:-Sampling Techniques – Part 2
- M4:-Bloom Filtering
- M4:-Bloom Filter Numerical in BDA
- M4:-FM ( Flajolet Martin ) Algorithm PYQ Numerical//
- M4:-Flajolet Martin Algorithm
- M4:-DGIM algorithm (Datar-Gionis-Indyk-Motwani Algorithm)
- M5:-Distance Measure
- M5:-Euclidean Distance
- M5:-Jaccard Distance
- M5:-Cosine Distance
- M5:-Edit Distance
- M5:-Hamming Distance
- M5:-Cure Algorithm
- M6:-Collaborative Filtering
- M6:-Dead Ends
- M6:-Clique and Community
- M6:-Clique and Community Numerical
- M6:-Content Based Recommendation System
- M6:-Authority and hub
- M6:-Determine the Communities Girvan Newman Sum VIMP
- M6:-Determine Communities Girvan Newman Numerical 2 VVIMP
- M6:Determine the Communities Girvan Newman PYQ #1//
- M6:Determine the Communities Girvan Newman PYQ #2//
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Big Data Notes and Importance
- Module 1 – Introduction to Big Data and Hadoop
- Module 2 – Hadoop HDFS and Map Reduce
- Module 3 – NoSql
- Module 4 – Mining Data Streams
- Module 5 – Real Time Big Data Model
- Module 6 – Data Analytics with R
- Module 1 – Big Data analytics Importance
- Module 2 – Hadoop HDFS and MapReduce
- Module 3 – NO SQL
- Module 4 – Mining Data Stream
- Module 5 – Real Time Big Data Models
- Module 6 – R Programming
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Big Data Analytics [viva]
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Natural Language Processing [Videos]
- Introduction to NLP [Natural Language Processing]
- Knowledge Required in NLP
- Ambiguity in NLP
- NLP Phases
- Regular Expression
- FSA
- Language Model
- Morphology Analysis
- N-gram Model
- Morphology Parsing
- Design FSA for Word of English 1 – 99
- NLP Bigram Numericals PYQ
- POS Tagging
- Syntax Analysis
- Tag-set for English
- Stochastic Part of Speech Tagging
- Transformation Based Tagging
- Multiple Tags ,Word and Unknown Words
- Basic Concept of Grammar and Parse Tree
- Parsing in NLP
- Hidden Markov Model Part 1
- Hidden Markov Model Part 2
- Viterbi Algorithm
- HMM Numerical-01 PYQ
- HMM Numerical 2 – PYQ
- Conditional Random Field CRF
- Introduction to Semantic Analysis
- Element of Semantic Analysis
- Attachment for Fragment of English (Phrases #1)
- Attachment for Fragment of English (Phrases #2)
- Attachment for Fragment of English (Phrases #3)
- WordNet
- Word Sense Disambiguation (WSD)
- Yarowsky Approach and HyperLex Approach
- Machine Translation Introduction
- Machine Translation Types
- Question Answering System
- Information Retrieval & the different steps in text processing for Information Retrieval
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Natural Language Processing [Notes]
- Introduction to NLP[Notes]
- Word Level Analysis [Notes]
- Syntax Analysis [Notes]
- Semantic Analysis [Notes]
- Pragmatics [Notes]
- Module 1 Introduction to NLP
- Module 2 WORD LEVEL ANALYSIS
- Module 3 SYNTAX ANALYSIS
- Module 4 SEMANTIC ANALYSIS
- Module 5 Pragmatic & Discourse Processing
- Module 6 Applications of NLP
- Viva Questions
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Deep Learning [Coming Soon]
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