Sem 7 Comps [ Mu ] [ Importance with Solution + Solution , Video Lectures + Viva Questions ]

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Machine Learning [Module 1]:- Introduction to Machine Learning 5
- Lecture2.1
  
  Introduction to Machine Learning 13 min
- Lecture2.2
  
  Key point in Machine Learning ( Basics of ML ) 10 min
- Lecture2.3
  
  Steps in Machine learning 05 min
- Lecture2.4
  
  Issue in Machine Learning 07 min
- Lecture2.5
  
  Overfitting and Underfitting Bias and Variance 20 min
Machine Learning [Module 2]:- Learning with Regression and Trees 8
- Lecture3.1
  
  Linear Regression 09 min
- Lecture3.2
  
  Logistic Regression part 1 09 min
- Lecture3.3
  
  Logistic Regression part 2 14 min
- Lecture3.4
  
  Decision Tree Sum using ID3 24 min
- Lecture3.5
  
  Decision Tree Sum using CART 22 min
- Lecture3.6
  
  Decision Tree Gini Index – Dec 2023 21 min
- Lecture3.7
  
  Decision Tree Gini Index – May 2024 12 min
- Lecture3.8
  
  Linear Regression Numerical Soln 10 min
Machine Learning [Module 3]:- Ensemble Learning 7
- Lecture4.1
  
  Ensemble learning 04 min
- Lecture4.2
  
  Boosting 04 min
- Lecture4.3
  
  Bagging 03 min
- Lecture4.4
  
  Stacking 02 min
- Lecture4.5
  
  Hard voting and Soft voting 03 min
- Lecture4.6
  
  Random Forest Algorithm 06 min
- Lecture4.7
  
  Cross Validation and K Fold 11 min
Machine Learning [Module 4]:- Learning with Classification 3
- Lecture5.1
  
  Support Vector Machine 10 min
- Lecture5.2
  
  Multiclass Classifications 08 min
- Lecture5.3
  
  Performance Evaluation metrics 26 min
Machine Learning [Module 5]:- Learning with clustering 7
- Lecture6.1
  
  K mean Clustering Algorithm 12 min
- Lecture6.2
  
  Apriori Algorithm with solved Example 12 min
- Lecture6.3
  
  Agglomerative Algorithm with solved Example Part #1 13 min
- Lecture6.4
  
  Agglomerative Algorithm with solved Example Part #2 05 min
- Lecture6.5
  
  DBSCAN – Density Based Clustering 08 min
- Lecture6.6
  
  Expectation Maximization Algorithm 06 min
- Lecture6.7
  
  Clustering with Minimal Spanning Tree 12 min
Machine Learning [Module 6]:- Dimensionality Reduction 6
- Lecture7.1
  
  Dimensionality Reduction 08 min
- Lecture7.2
  
  PCA (Principal component Analysis ) Concept 14 min
- Lecture7.3
  
  PCA (Principal Component Analysis) Sum 19 min
- Lecture7.4
  
  LDA Numericals 28 min
- Lecture7.5
  
  Matrix Diagonalization 26 min
- Lecture7.6
  
  Numericals on SVD 25 min
Machine Learning [Module wise Imp Solution] 6
- Lecture8.1
  
  Introduction to Machine Learning
- Lecture8.2
  
  Learning with Regression and Trees
- Lecture8.3
  
  Ensemble Learning
- Lecture8.4
  
  Learning with Classification
- Lecture8.5
  
  Learning with clustering
- Lecture8.6
  
  Dimensionality Reduction
Big Data Analytics [Module 1]:- Introduction to Big Data and Hadoop 3
- Lecture9.1
  
  Introduction to Big Data Analytics 07 min
- Lecture9.2
  
  Introduction to Hadoop Part #1 10 min
- Lecture9.3
  
  Introduction to Hadoop Part #2 10 min
Big Data Analytics [Module 2]:- Hadoop HDFS and Map Reduce 2
- Lecture10.1
  
  Introduction to MapReduce 11 min
- Lecture10.2
  
  Matrix Multiplication 16 min
Big Data Analytics [Module 3]:- NoSql 5
- Lecture11.1
  
  Introduction to No SQL Database 08 min
- Lecture11.2
  
  Key-Value Stores 07 min
- Lecture11.3
  
  Column Store Database 06 min
- Lecture11.4
  
  Document Database 05 min
- Lecture11.5
  
  Graph Database 07 min
Big Data Analytics [Module 4]:- Mining Data Streams 7
- Lecture12.1
  
  Data Stream Management System 08 min
- Lecture12.2
  
  Sampling Techniques – Part 1 07 min
- Lecture12.3
  
  Sampling Techniques – Part 2 07 min
- Lecture12.4
  
  Bloom Filtering 18 min
- Lecture12.5
  
  Bloom Filter Numerical in BDA 23 min
- Lecture12.6
  
  Flajolet Martin Algorithm 14 min
- Lecture12.7
  
  DGIM algorithm (Datar-Gionis-Indyk-Motwani Algorithm) 09 min
Big Data Analytics [Module 5]:- Real Time Big Data Model 8
- Lecture13.1
  
  Dead Ends 04 min
- Lecture13.2
  
  Clique and Community 14 min
- Lecture13.3
  
  Clique and Community Numerical 20 min
- Lecture13.4
  
  Content Based Recommendation System 18 min
- Lecture13.5
  
  Collaborative Filtering 20 min
- Lecture13.6
  
  Authority and hub 21 min
- Lecture13.7
  
  Determine the Communities Girvan Newman Sum VIMP 23 min
- Lecture13.8
  
  Determine Communities Girvan Newman Numerical 2 VVIMP 17 min
Big Data Analytics [Module 6]:- Data Analytics with R 13
- Lecture14.1
  
  Introduction – R Programming – #1 12 min
- Lecture14.2
  
  Introduction – R Programming – #2 16 min
- Lecture14.3
  
  Vectors in R Programming 17 min
- Lecture14.4
  
  Objects in R Programming 18 min
- Lecture14.5
  
  Interacting With Users 12 min
- Lecture14.6
  
  Script in R 13 min
- Lecture14.7
  
  Plot in R 17 min
- Lecture14.8
  
  Exploring Dataset in R 25 min
- Lecture14.9
  
  Function in R 12 min
- Lecture14.10
  
  Visualization in R 16 min
- Lecture14.11
  
  Module 6 Numerical IMP Solution 27 min
- Lecture14.12
  
  R Programing Numerical Dec 2023 34 min
- Lecture14.13
  
  R Programming Numerical PYQ Part 2 27 min
Big Data Analytics [Module wise Notes] 6
- Lecture15.1
  
  Module 1 – Introduction to Big Data and Hadoop
- Lecture15.2
  
  Module 2 – Hadoop HDFS and Map Reduce
- Lecture15.3
  
  Module 3 – NoSql
- Lecture15.4
  
  Module 4 – Mining Data Streams
- Lecture15.5
  
  Module 5 – Real Time Big Data Model
- Lecture15.6
  
  Module 6 – Data Analytics with R
Big Data Analytics [Module wise IMP Solution] 7
- Lecture16.1
  
  Module 1 – Big Data analytics Importance
- Lecture16.2
  
  Module 2 – Hadoop HDFS and MapReduce
- Lecture16.3
  
  Module 3 – NO SQL
- Lecture16.4
  
  Module 4 – Mining Data Stream
- Lecture16.5
  
  Module 5 – Real Time Big Data Models
- Lecture16.6
  
  Module 6 – R Programming
- Lecture16.7
  
  Big Data IMP Solution 2024
Natural Language Processing [Module 1]:- Introduction to NLP 5
- Lecture17.1
  
  Introduction to NLP [Natural Language Processing] 12 min
- Lecture17.2
  
  Knowledge Required in NLP 11 min
- Lecture17.3
  
  Ambiguity in NLP 07 min
- Lecture17.4
  
  NLP Phases 08 min
- Lecture17.5
  
  Introduction to NLP[Notes]
Natural Language Processing [Module 2]:- Word Level Analysis 9
- Lecture18.1
  
  Regular Expression 09 min
- Lecture18.2
  
  FSA 09 min
- Lecture18.3
  
  Language Model 10 min
- Lecture18.4
  
  Morphology Analysis 11 min
- Lecture18.5
  
  N-gram Model 04 min
- Lecture18.6
  
  Morphology Parsing 09 min
- Lecture18.7
  
  Word Level Analysis [Notes]
- Lecture18.8
  
  Design FSA for Word of English 1 – 99 05 min
- Lecture18.9
  
  NLP Bigram Numericals PYQ 29 min
Natural Language Processing [Module 3]:- Syntax analysis 16
- Lecture19.1
  
  POS Tagging 10 min
- Lecture19.2
  
  Syntax Analysis 03 min
- Lecture19.3
  
  Tag-set for English 12 min
- Lecture19.4
  
  Rule Based POS 06 min
- Lecture19.5
  
  Stochastic Part of Speech Tagging 08 min
- Lecture19.6
  
  Transformation Based Tagging 06 min
- Lecture19.7
  
  Multiple Tags ,Word and Unknown Words 04 min
- Lecture19.8
  
  Basic Concept of Grammar and Parse Tree 09 min
- Lecture19.9
  
  Parsing in NLP 06 min
- Lecture19.10
  
  Hidden Markov Model Part 1 10 min
- Lecture19.11
  
  Hidden Markov Model Part 2 07 min
- Lecture19.12
  
  Viterbi Algorithm 08 min
- Lecture19.13
  
  HMM Numerical-01 PYQ 36 min
- Lecture19.14
  
  HMM Numerical 2 – PYQ 29 min
- Lecture19.15
  
  Conditional Random Field CRF 20 min
- Lecture19.16
  
  Syntax Analysis [Notes]
Natural Language Processing [Module 4]:- Semantic Analysis 9
- Lecture20.1
  
  Introduction to Semantic Analysis 13 min
- Lecture20.2
  
  Element of Semantic Analysis 06 min
- Lecture20.3
  
  Attachment for Fragment of English (Phrases #1) 09 min
- Lecture20.4
  
  Attachment for Fragment of English (Phrases #2) 05 min
- Lecture20.5
  
  Attachment for Fragment of English (Phrases #3) 04 min
- Lecture20.6
  
  WordNet 08 min
- Lecture20.7
  
  Word Sense Disambiguation (WSD) 09 min
- Lecture20.8
  
  Yarowsky Approach and HyperLex Approach 15 min
- Lecture20.9
  
  Semantic Analysis [Notes]
Natural Language Processing [Module 5]:- Pragmatic & Discourse Processing 5
- Lecture21.1
  
  Pragmatics 13 min
- Lecture21.2
  
  Discourse Processing 16 min
- Lecture21.3
  
  Pragmatics [Notes]
- Lecture21.4
  
  CYK Numerical 11 min
- Lecture21.5
  
  Anaphora Resolution Using Hobbs’ Algo 13 min
Natural Language Processing [Module 6]:- Application of NLP 4
- Lecture22.1
  
  Machine Translation Introduction 14 min
- Lecture22.2
  
  Machine Translation Types 23 min
- Lecture22.3
  
  Question Answering System 20 min
- Lecture22.4
  
  Information Retrieval & the different steps in text processing for Information Retrieval 25 min
Natural Language Processing [Module wise Imp Solution] 6
- Lecture23.1
  
  Module 1 Introduction to NLP
- Lecture23.2
  
  Module 2 WORD LEVEL ANALYSIS
- Lecture23.3
  
  Module 3 SYNTAX ANALYSIS
- Lecture23.4
  
  Module 4 SEMANTIC ANALYSIS
- Lecture23.5
  
  Module 5 Pragmatic & Discourse Processing
- Lecture23.6
  
  Module 6 Applications of NLP
Natural Language Processing [Viva Questions] 1
- Lecture24.1
  
  Viva Questions

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About the Course 0

This Course is Only Available On Our App

Machine Learning [Module 1]:- Introduction to Machine Learning 5

Machine Learning [Module 2]:- Learning with Regression and Trees 8

Machine Learning [Module 3]:- Ensemble Learning 7

Machine Learning [Module 4]:- Learning with Classification 3

Machine Learning [Module 5]:- Learning with clustering 7

Machine Learning [Module 6]:- Dimensionality Reduction 6

Machine Learning [Module wise Imp Solution] 6

Big Data Analytics [Module 1]:- Introduction to Big Data and Hadoop 3

Big Data Analytics [Module 2]:- Hadoop HDFS and Map Reduce 2

Big Data Analytics [Module 3]:- NoSql 5

Big Data Analytics [Module 4]:- Mining Data Streams 7

Big Data Analytics [Module 5]:- Real Time Big Data Model 8

Big Data Analytics [Module 6]:- Data Analytics with R 13

Big Data Analytics [Module wise Notes] 6

Big Data Analytics [Module wise IMP Solution] 7

Natural Language Processing [Module 1]:- Introduction to NLP 5

Natural Language Processing [Module 2]:- Word Level Analysis 9

Natural Language Processing [Module 3]:- Syntax analysis 16

Natural Language Processing [Module 4]:- Semantic Analysis 9

Natural Language Processing [Module 5]:- Pragmatic & Discourse Processing 5

Natural Language Processing [Module 6]:- Application of NLP 4

Natural Language Processing [Module wise Imp Solution] 6

Natural Language Processing [Viva Questions] 1

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