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Machine Learning [Module 1]:- Introduction to Machine Learning 4
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Introduction to Machine Learning 13 minLecture1.1
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Key point in Machine Learning ( Basics of ML ) 10 minLecture1.2
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Steps in Machine learning 05 minLecture1.3
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Issue in Machine Learning 07 minLecture1.4
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Machine Learning [Module 2]:- Learning with Regression and Trees 7
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Linear Regression 09 minLecture2.1
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Logistic Regression part 1 14 minLecture2.2
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Logistic Regression part 2 14 minLecture2.3
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Decision Tree Sum using ID3 24 minLecture2.4
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Decision Tree Sum using CART 22 minLecture2.5
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Decision Tree Gini Index – Dec 2023 21 minLecture2.6
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Decision Tree Gini Index – May 2024 12 minLecture2.7
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Machine Learning [Module 3]:- Ensemble Learning 6
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Ensemble learning 04 minLecture3.1
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Boosting 04 minLecture3.2
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Bagging 03 minLecture3.3
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Stacking 02 minLecture3.4
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Hard voting and Soft voting 03 minLecture3.5
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Random Forest Algorithm 06 minLecture3.6
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Machine Learning [Module 4]:- Learning with Classification 2
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Support Vector Machine 10 minLecture4.1
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Multiclass Classifications 08 minLecture4.2
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Machine Learning [Module 5]:- Learning with clustering 7
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K mean Clustering Algorithm 12 minLecture5.1
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Apriori Algorithm with solved Example 12 minLecture5.2
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Agglomerative Algorithm with solved Example Part #1 13 minLecture5.3
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Agglomerative Algorithm with solved Example Part #2 05 minLecture5.4
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DBSCAN – Density Based Clustering 08 minLecture5.5
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Expectation Maximization Algorithm 06 minLecture5.6
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Clustering with Minimal Spanning Tree 12 minLecture5.7
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Machine Learning [Module 6]:- Dimensionality Reduction 4
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Dimensionality Reduction 08 minLecture6.1
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PCA (Principal component Analysis ) Concept 14 minLecture6.2
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PCA (Principal Component Analysis) Sum 19 minLecture6.3
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LDA Numericals 28 minLecture6.4
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Machine Learning [Module wise Imp Solution] 6
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Introduction to Machine LearningLecture7.1
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Learning with Regression and TreesLecture7.2
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Ensemble LearningLecture7.3
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Learning with ClassificationLecture7.4
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Learning with clusteringLecture7.5
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Dimensionality ReductionLecture7.6
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Big Data Analytics [Module 1]:- Introduction to Big Data and Hadoop 3
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Introduction to Big Data Analytics 07 minLecture8.1
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Introduction to Hadoop Part #1 10 minLecture8.2
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Introduction to Hadoop Part #2 10 minLecture8.3
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Big Data Analytics [Module 2]:- Hadoop HDFS and Map Reduce 2
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Introduction to MapReduce 11 minLecture9.1
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Matrix Multiplication 16 minLecture9.2
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Big Data Analytics [Module 3]:- NoSql 5
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Introduction to No SQL Database 08 minLecture10.1
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Key-Value Stores 07 minLecture10.2
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Column Store Database 06 minLecture10.3
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Document Database 05 minLecture10.4
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Graph Database 07 minLecture10.5
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Big Data Analytics [Module 4]:- Mining Data Streams 6
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Data Stream Management System 08 minLecture11.1
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Sampling Techniques – Part 1 07 minLecture11.2
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Sampling Techniques – Part 2 07 minLecture11.3
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Bloom Filtering 18 minLecture11.4
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Flajolet Martin Algorithm 14 minLecture11.5
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DGIM algorithm (Datar-Gionis-Indyk-Motwani Algorithm) 09 minLecture11.6
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Big Data Analytics [Module 5]:- Real Time Big Data Model 4
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Dead Ends 04 minLecture12.1
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Clique and Community 14 minLecture12.2
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Collaborative Filtering 20 minLecture12.3
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Authority and hub 21 minLecture12.4
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Big Data Analytics [Module 6]:- Data Analytics with R 12
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Introduction – R Programming – #1 12 minLecture13.1
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Introduction – R Programming – #2 16 minLecture13.2
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Vectors in R Programming 17 minLecture13.3
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Objects in R Programming 18 minLecture13.4
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Interacting With Users 12 minLecture13.5
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Script in R 13 minLecture13.6
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Plot in R 17 minLecture13.7
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Exploring Dataset in R 25 minLecture13.8
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Function in R 12 minLecture13.9
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Visualization in R 16 minLecture13.10
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Module 6 Numerical IMP Solution 27 minLecture13.11
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R Programing Numerical Dec 2023 34 minLecture13.12
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Big Data Analytics [Module wise Notes] 6
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Module 1 – Introduction to Big Data and HadoopLecture14.1
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Module 2 – Hadoop HDFS and Map ReduceLecture14.2
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Module 3 – NoSqlLecture14.3
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Module 4 – Mining Data StreamsLecture14.4
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Module 5 – Real Time Big Data ModelLecture14.5
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Module 6 – Data Analytics with RLecture14.6
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Big Data Analytics [Module wise IMP Solution] 7
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Module 1 – Big Data analytics ImportanceLecture15.1
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Module 2 – Hadoop HDFS and MapReduceLecture15.2
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Module 3 – NO SQLLecture15.3
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Module 4 – Mining Data StreamLecture15.4
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Module 5 – Real Time Big Data ModelsLecture15.5
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Module 6 – R ProgrammingLecture15.6
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Big Data IMP Solution 2024Lecture15.7
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Natural Language Processing [Module 1]:- Introduction to NLP 5
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Introduction to NLP [Natural Language Processing] 12 minLecture16.1
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Knowledge Required in NLP 11 minLecture16.2
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Ambiguity in NLP 07 minLecture16.3
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NLP Phases 08 minLecture16.4
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Introduction to NLP[Notes]Lecture16.5
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Natural Language Processing [Module 2]:- Word Level Analysis 7
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Regular Expression 09 minLecture17.1
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FSA 09 minLecture17.2
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Language Model 10 minLecture17.3
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Morphology Analysis 11 minLecture17.4
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N-gram Model 04 minLecture17.5
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Morphology Parsing 09 minLecture17.6
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Word Level Analysis [Notes]Lecture17.7
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Natural Language Processing [Module 3]:- Syntax analysis 13
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POS Tagging 10 minLecture18.1
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Syntax Analysis 03 minLecture18.2
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Tag-set for English 12 minLecture18.3
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Rule Based POS 06 minLecture18.4
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Stochastic Part of Speech Tagging 08 minLecture18.5
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Transformation Based Tagging 06 minLecture18.6
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Multiple Tags ,Word and Unknown Words 04 minLecture18.7
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Basic Concept of Grammar and Parse Tree 09 minLecture18.8
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Parsing in NLP 06 minLecture18.9
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Hidden Markov Model Part 1 10 minLecture18.10
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Hidden Markov Model Part 2 07 minLecture18.11
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Viterbi Algorithm 08 minLecture18.12
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Syntax Analysis [Notes]Lecture18.13
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Natural Language Processing [Module 4]:- Semantic Analysis 8
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Introduction to Semantic Analysis 13 minLecture19.1
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Element of Semantic Analysis 06 minLecture19.2
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Attachment for Fragment of English (Phrases #1) 09 minLecture19.3
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Attachment for Fragment of English (Phrases #2) 05 minLecture19.4
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Attachment for Fragment of English (Phrases #3) 04 minLecture19.5
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WordNet 08 minLecture19.6
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Word Sense Disambiguation (WSD) 09 minLecture19.7
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Semantic Analysis [Notes]Lecture19.8
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Natural Language Processing [Module 5]:- Pragmatic & Discourse Processing 3
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Pragmatics 13 minLecture20.1
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Discourse Processing 16 minLecture20.2
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Pragmatics [Notes]Lecture20.3
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Natural Language Processing [Module wise Imp Solution] 6
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Module 1 Introduction to NLPLecture21.1
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Module 2 WORD LEVEL ANALYSISLecture21.2
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Module 3 SYNTAX ANALYSISLecture21.3
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Module 4 SEMANTIC ANALYSISLecture21.4
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Module 5 Pragmatic & Discourse ProcessingLecture21.5
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Module 6 Applications of NLPLecture21.6
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Natural Language Processing [Viva Questions] 1
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Viva QuestionsLecture22.1
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