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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:-Graph Database
- M3:-Document 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:-Flajolet Martin Algorithm
- M4:-FM ( Flajolet Martin ) Algorithm PYQ Numerical
- 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 PYQ #1
- M6:Determine the Communities Girvan Newman PYQ #2
- M6:-Determine Communities Girvan Newman Numerical 2 VVIMP
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Big Data Notes and Importance
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Big Data Analytics [viva]
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Natural Language Processing [Videos]
- Module 1 :- Introduction to NLP [Natural Language Processing]
- Module 1 :- Knowledge Required in NLP
- Module 1 :- NLP Phases
- Module 1 :- Ambiguity in NLP
- Module 2 : – Regular Expression
- Module 2 : – FSA
- Module 2 : – Language Model
- Module 2 : – Design FSA for Word of English 1 – 99
- Module 2 : – Language Model
- Module 2 : – Morphology Analysis
- Module 2 : – Morphology Parsing
- Module 2 : – N-gram Model
- Module 2 : – NLP Bigram Numericals PYQ
- Module 3 : – POS Tagging
- Module 3 : – Rule Based POS
- Module 3 : – Stochastic Part of Speech Tagging
- Module 3 : – Transformation Based Tagging
- Module 3 : – Syntax Analysis
- Module 3 : – Tag-set for English
- Module 3 : – Basic Concept of Grammar and Parse Tree
- Module 3 : – Multiple Tags ,Word and Unknown Words
- Module 3 : – Parsing in NLP
- Module 3 : – Hidden Markov Model Part 1
- Module 3 : – Hidden Markov Model Part 2
- Module 3 : – Viterbi Algorithm
- Module 3 : – HMM Numerical-01 PYQ
- Module 3 : – HMM Numerical 2 – PYQ
- Module 3 : – Conditional Random Field CRF
- Module 4 : Element of Semantic Analysis
- Module 4 : Attachment for Fragment of English (Phrases #1)
- Module 4 : Attachment for Fragment of English (Phrases #2)
- Module 4 : Attachment for Fragment of English (Phrases #3)
- Module 4 : Introduction to Semantic Analysis
- Module 4 : WordNet
- Module 4 : Word Sense Disambiguation (WSD)
- Module 4 : Yarowsky Approach and HyperLex Approach
- Module 5 : Discourse Processing
- Module 5 : Pragmatics
- Module 5 : CYK Numerical
- Module 5 : Anaphora Resolution Using Hobbs’ Algo
- Module 6 : Machine Translation Introduction
- Module 6 : Machine Translation Types
- Module 6 : Question Answering System
- Module 6 : Information Retrieval & the different steps in text processing for Information Retrieval
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Natural Language Processing [Notes]
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Deep Learning [Videos]
- M1 : Fundamentals of Neural Network :- Perceptron-model
- M1 : Fundamentals of Neural Network :- Multilayer-Perceptron-Model-ML
- M1 : Fundamentals of Neural Network :- Deep Learning + Neural Network & Types
- M1 : Fundamentals of Neural Network :- Activation-Function
- Module 2 : Training Optimization Regularization :- Activation functions & its Types
- Module 2 : Training Optimization Regularization :- Loss Function & its Types
- Module 2 : Training Optimization Regularization :- Back Propogation
- Module 2 : Training Optimization Regularization :- Gradient-Descent
- Module 2 : Training Optimization Regularization :- Droupout
- Module 2 : Training Optimization Regularization :- Overfitting & Underfitting
- Module 2 : Training Optimization Regularization :- regularization
- Module 3 : Autoencoders :- Autoencoder
- Module 3 : Autoencoders :- Type of Autoencoder
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Deep Learning [Notes + Importance ]
- Deep Learning Modulewise Importance
- Deep Learning Importance Solution
- Notes:- Module 1 – Fundamentals of Neural Network
- Notes:- M2 – Training, Optimization and Regularization of Deep Neural Networks
- Notes:- M3 – Autoencoders
- IMP :- M1 – Fundamentals of Neural Network.
- IMP :- M2 – Training, Optimization and Regularization
- IMP :- M3 – Autoencoders
- IMP :- M4 – Convolutional Neural Networks
- IMP :- M5 – Recurrent Neural Networks
- IMP :- M6 – Recent Trends and Applications (GAN)
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M5:-Cosine Distance