Exercise 5 - Company Data Base
Sunil Gl
Exercise 5 - Company Data Base
17:15
Module-1 Strong Entity & Weak Entity
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Module-1 Strong Entity & Weak Entity
4:17
Charactristics of Data Base Approach
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Charactristics of Data Base Approach
7:33
Exercise-02 ORDER Data Base
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Exercise-02 ORDER Data Base
15:29
EXercise 2 -ORDER Data Base
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EXercise 2 -ORDER Data Base
1:40
Module-1 DBMS Introduction
Sunil Gl
Module-1 DBMS Introduction
6:14
16 November 2021
Sunil Gl
16 November 2021
4:18
Module 5 - Social Network Analysis
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Module 5 - Social Network Analysis
16:53
Module 5 - Web Mining
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Module 5 - Web Mining
15:49
Program 5 Intermediate Code Generation in Three Address Form
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Program 5 Intermediate Code Generation in Three Address Form
41:55
06 Program 4 Shift Reduce Parsing Technique
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06 Program 4 Shift Reduce Parsing Technique
26:20
Program -3 Predictive /LL(1) Parsing Table
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Program -3 Predictive /LL(1) Parsing Table
32:36
Module-05 Syntax Directed Translation
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Module-05 Syntax Directed Translation
35:14
Module-5 Intermediate Code Generation
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Module-5 Intermediate Code Generation
22:58
Module -5 Evaluation Order of SDD
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Module -5 Evaluation Order of SDD
36:36
Module -5 Text Mining
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Module -5 Text Mining
24:13
Module-4 Cluster Analysis
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Module-4 Cluster Analysis
29:38
Module-4 Regression
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Module-4 Regression
27:48
Module-4 Decision Tree
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Module-4 Decision Tree
23:57
Module -3 Data Mining
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Module -3 Data Mining
36:49
Module-3 Data Warehousing
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Module-3 Data Warehousing
25:57
Module 3 Business Intelligence Concepts and Applications
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Module 3 Business Intelligence Concepts and Applications
39:49
Module -2 Managing Hadoop with Apache Ambari
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Module -2 Managing Hadoop with Apache Ambari
23:46
Module-2 Hadoop Yarn Applications
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Module-2 Hadoop Yarn Applications
16:49
Module 2 Essential Hadoop Tools
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Module 2 Essential Hadoop Tools
33:44
Module -1 Hadoop Distributed File System
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Module -1 Hadoop Distributed File System
29:16
Module -1 Hadoop for Big Data
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Module -1 Hadoop for Big Data
29:06
Module - 2 Hadoop for Big Data
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Module - 2 Hadoop for Big Data
29:06
Module -1 Introductions to Big Data
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Module -1 Introductions to Big Data
32:53
Module - 3 Left Recursion
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Module - 3 Left Recursion
36:22
Module-3 Parsing Techniques
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Module-3 Parsing Techniques
33:37
Module-3 Eliminating Ambiguity of CFG
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Module-3 Eliminating Ambiguity of CFG
37:52
Module -3 Parse Tree
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Module -3 Parse Tree
19:02
Module-2 Input Buffering
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Module-2 Input Buffering
40:55
Module-2 Lexical Analysis
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Module-2 Lexical Analysis
33:50
Module -2 Grouping of Phases into Passes
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Module -2 Grouping of Phases into Passes
33:56
Module-2 Compilers & Language Processors
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Module-2 Compilers & Language Processors
38:12
Module-1 Pass-2 of the Assemble Algorithm
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Module-1 Pass-2 of the Assemble Algorithm
30:24
Module-1 Pass-1 of the Assembler Algorithm
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Module-1 Pass-1 of the Assembler Algorithm
29:36
Module-1 Data Structures used in Design of Assemblers
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Module-1 Data Structures used in Design of Assemblers
23:13
Module-1 Assemblers
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Module-1 Assemblers
38:14
12. STRING CONSTANT
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12. STRING CONSTANT
11:43
11. ESCAPE SEQUENCE
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11. ESCAPE SEQUENCE
7:35
10. CHARACTER CONSTANT
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10. CHARACTER CONSTANT
6:53
09. FLOAT CONSTANT
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09. FLOAT CONSTANT
10:11
08. ENUMERATED CONSTANTS
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08. ENUMERATED CONSTANTS
8:00
07. RANGE SIGNED
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07. RANGE SIGNED
14:30
06 RANGE UNSIGNED
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06 RANGE UNSIGNED
12:40
05. CONSTANTS
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05. CONSTANTS
4:04
04 IDENTIFIERS
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04 IDENTIFIERS
10:19
03. KEYWORDS
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03. KEYWORDS
9:31
02. TOKENS
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02. TOKENS
5:55
01. ALPHABETS
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01. ALPHABETS
12:11
14. Reinforcement Learning and Q Learning
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14. Reinforcement Learning and Q Learning
26:55
P04 ANN (Program 4)
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P04 ANN (Program 4)
31:04
13 K Mean Problem
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13 K Mean Problem
10:20
P06 Naive Bayse (Lab Program 06)
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P06 Naive Bayse (Lab Program 06)
9:37
P06 Naive Bayse Classifier (Program06 theory)
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P06 Naive Bayse Classifier (Program06 theory)
11:48
06 Naive Bayes Classifier ( Program 06 Theory)
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06 Naive Bayes Classifier ( Program 06 Theory)
9:37
P05&07 Naive Bayse Classiifer (Program 05 and 07)
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P05&07 Naive Bayse Classiifer (Program 05 and 07)
41:48
P02 Candidate Elimination Algorithm (Program 2)
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P02 Candidate Elimination Algorithm (Program 2)
10:54
P02 Candidate elimination algorithm Theory(program 2)
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P02 Candidate elimination algorithm Theory(program 2)
11:44
P10. Locally Weighted Regression(Program 10)
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P10. Locally Weighted Regression(Program 10)
39:42
Regression and Locally Weighted Regression
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Regression and Locally Weighted Regression
24:46
10. K Nearest Neighbour Classifier
Sunil Gl
10. K Nearest Neighbour Classifier
21:16
P09 K Nearest Neighbour algorithm( Program 09)
Sunil Gl
P09 K Nearest Neighbour algorithm( Program 09)
29:27
P08 K Mean & EM Algorithm (Program 08)
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P08 K Mean & EM Algorithm (Program 08)
12:57
P08 K Mean & Gaussian Mixture (Program 8)
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P08 K Mean & Gaussian Mixture (Program 8)
17:24
P08 K Mean & Gaussian Mixture (Program 08 theory)
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P08 K Mean & Gaussian Mixture (Program 08 theory)
17:24
P09 K Mean & EM Algorithm Program
Sunil Gl
P09 K Mean & EM Algorithm Program
12:57
10 K Mean & Gaussian Mixture
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10 K Mean & Gaussian Mixture
17:24
MODULE 1 ALPHABETS
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MODULE 1 ALPHABETS
12:11
C CH 01 15 ESCAPE SEQUENCE
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C CH 01 15 ESCAPE SEQUENCE
7:35
P01 Find S algorithm
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P01 Find S algorithm
17:51
PO3 Decision Tree
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PO3 Decision Tree
40:58
03 Anaconda Framework
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03 Anaconda Framework
32:10
09 ANN Introduction
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09 ANN Introduction
30:07
08 Decision tree boolean function problem
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08 Decision tree boolean function problem
22:56
07 Decision tree problem
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07 Decision tree problem
45:55
05 Decision tree introduction and appropriate problems for Decision Tree
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05 Decision tree introduction and appropriate problems for Decision Tree
36:34
04 Decision tree Introduction
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04 Decision tree Introduction
36:34
03 Anaconda Framework
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03 Anaconda Framework
32:10
02 ML Introduction
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02 ML Introduction
33:22
01 ML Introduction
Sunil Gl
01 ML Introduction
39:35