14:54

Part 2: Logistic Regression, Gradient Descent, Log Likelihood, Sigmoid

15:47

Logistic Regression, Sigmoid Function, Binary Classification

11:42

Multivariate Linear Regression, Multivariate Simple & Multiple Linear Regression

25:04

Linear Regression: Gradient Descent Approach, Learning rate, parameters, Simple and Multiple

14:50

Part 6: Support Vector Machine, Soft Margin, Parameter C, Ski, Penalty, Lineraly separable

12:43

Part V: Support Vector Machine, Quadratic Programming, Toy Example, Support vectors,w,b,unseen sampl

09:11

Part IVSupport Vector MachineQuadratic Programming

10:06

Part 3: Support Vector Machine, Multiclass, One-vs-Rest, One-vs-One, Error-Correcting Output Codes

12:36

Part 2: Support Vector Machine, Margin, Support Vectors, Boundary, Hyperplane, Simple Problem in 2D

23:30

Part 1: Support Vector Machine, Constrained Optimization, Margin, Support vectors, decision boundary

03:39

Part 9: Introduction to NLP, Challenges in NLP Application development

12:01

Part 8: Introduction to NLP at various Levels in Marathi and Hindi Languages

12:23

PART 7: Introduction to NLP, Ambiguities, English, Lexical, Syntactic, Semantic, Pragmatic,Discourse

15:29

Part 6: Introduction to NLP, Why NLP is hard, Textual Humor, Sarcasm, Idioms, Neologisms, Tokenizati

03:48

Part 5: Introduction to NLP, Language, Grammar and Knowledge in NLP

06:50

Part 4: History of NLP, First Era, Second Era, Third Era, Fourth Era, Introduction to NLP

08:24

NLP Introduction Part 3: Generic NLP System, Parser, Semantic, Pragmatic. & Discourse, Reasoner

13:13

NLP Introduction Part I:Definition,Natural Language Generation(NLG)& Understanding(NLU), Need, Goals

15:48

Semantic Analysis Part 3:Relations among lexemes & their Senses, NLP, Homonymy, Polysemy, Syno,

11:12

Part 1: Semantic Analysis, NLP, Computational, Distributional, Formal Semantics, Lexicon & Lexeme

10:08

Part 7: Earley Parser, Top Down Parser, NLP, Predict, Scan, Complete, Chart, Tanle, CFG Rule

18:25

Parser Part 6: Predictive Parser, Top Down, NLP, First, Follow, Stack, look ahead, Predictive Table

15:22

Part 5: PCFG parser, Bottom Up Parser, NLP, CFG, Probability, CYK Algorithm, Parse Trees Exercises

12:57

Part 4: Bottom Up Parser, Shift Reduce Parser, Stack, Shift, Reduce, Ambiguity, Backtracking exercis

20:59

Parser Part 3: Bottom Up, COCKE-YOUNGER-KASAMI (CYK or CKY Parser), NLP, CNF, CFG, Tree, Dynamic

17:57

Part 2: NLP Parsers, Modelling Constituency, CFG, Chomsky Normal Form (CNF), Top down & Bottom Up

11:50

Part 1: Parsers in NLP, Parsers Role,Words & Word Groups (Constituency),Types of Parsers, Ambiguity

26:31

Good Turing Discounting, Smoothing, C*, P*GT, Backoff, Interpolation, Laplace, MLE, NLP

19:44

Part 6: Image Processing Introduction, Connectivity, Adjacency, Euclidean, City Block, Chess Board,m

09:10

Part 5: Image Processing Introduction, IMAGE FILE FORMATS, TIFF, BMP, JPEG, Features, Adv, Disadv

21:12

Part 4 : Image Processing, Sampling, Quantization, Spatial Resolution, Gray level, Tonal resolution

06:04

Part 3: Components or Elements of an Image Processing System

16:57

Part 2: Fundamental Steps in Image Processing, Acquisition, Filtering, Segmentation, Morphology

10:27

Part 1: Introduction to Image Processing, Definition Digital image, Applications,

20:19

Part 6: Image Transform, Fast Hadamard Transform, Butterfly approach, Complexity N*log2(N-1)

16:03

Part 5: Image Transform, Discrete Cosine Transform, DCT, Basis images, Cosine, Real, Low, High freq

10:32

Part 4: Image Transform, Fourier Transform, Exercise on 1D, 2D signal, Forward & Backward Transform

10:24

Part 3: Image Transform: Walsh Transform, Increasing sign change, Exercises on 1D, 2D, WHT

14:55

Part 2: Image Transform, Walsh Hadamard Transform ,1D signal, 2D Signal, Recursive generation

15:41

Part 1: Image Transform, Introduction, Unitary Transform, Orthogonal Transform, Kronecker Product

18:02

Part 7: Image Compression, Lossy, Vector Quantization, R,L, Code book, Code vectors, Image Vectors

13:11

Part 6: Image Compression, Improved Gray scale Quantization, False contouring, MSB, LSB

20:15

Part 5: Image Compression, Lossless, Arithmetic Encoding, and Lossless Predictive Coding (DPCM)

18:18

Part 4: Image Compression, Lossless Compression. Pack-bits Encoding (RLE), Huffman Encoding

18:31

Part 3: Image Compression, Fidelity Criteria, Subjective, Objective, Entropy, Encoder, Decoder

21:07

Part 2: Image Compression: Redundancies : Coding, Interpixel, Psycho-visual , quantization

13:01

Part I : Image Compression, Intro, Lossless and Lossy, Compression Ratio Relative Data Redundancy

14:26

Edge Detection Operators, Segmentation Ordinary, Robert, Prewitt, Sobel, First Order Derivative

16:52

Segmentation based on Discontinuities (Point, Line, Edge), First Order & Second Order Derivative

12:03

Segmentation, Image Processing, Point, Line Detection, Discontinuity, Similarity

14:25

Query Expansion through Local Clustering, Query Expansion through Local Context Analysis

20:24

Evaluation of Information Retrieval System, Assessing Relevance, and Kappa Statistics

12:31

Evaluation Ranked Retrieval, Sensitivity Specificity, ROC, CG, Normalized Discounted Cumulative Gain

30:55

Evaluation of Ranked Retrieval, Information Retrieval, AP, MAP, 11 point precision Recall

28:36

Evaluation of Information Retrieval System, Unranked Retrieval Precision, Recall, Accuracy, F1-Score

11:03

Evaluation of Information Retrieval System and Standard Test Collections, Information Retrieval

12:40

Suffix Automation, BDM Algorithm, Sequential Search, Information Retrieval, DAWG

07:27

Shift OR Algorithm, Left shift, Bitwise OR, Sequential Search, Information Retrieval

22:38

Sequential Search, Brute Force Algorithm, Knuth-Morris-Pratt Algorithm, Aho-Corasick Algorithm, IRS

26:38

Suffix Arrays and Suffix Trees, Indexing in Information Retrieval, TRIE, Supra Index, binary search