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Learn MoreInformation Retrieval and Mining Massive Data Sets
Rating: 4.3 out of 54.3(150 ratings)
2,232 students
Information Retrieval and Mining Massive Data Sets
Learn various techniques to build a Google scale Information Retrieval System.
Created byOmkar Deshpande, Mentors Net
Last updated 9/2017
English
What you'll learn
- The course is primarily divided into 6 parts.
- Part 1: Building an Information Retrieval System
- Part 2: Mining Frequent Patterns and Associations
- Part 3: Classification and Clustering
- Part 4: Web Mining
- Part 5: Recommendation Systems
Explore related topics
Course content
14 sections • 123 lectures • 39h 8m total length
- What is Data Mining7:59
In This Video We Describe What Is Data Mining
- Structured Data, Unstructured data and Information Retrieval16:57
In This Video We Talk About Structured Data, Unstructured Data and Information Retrieval
- Term-Document Incidence Matrix (1)6:37
In This Video We Describe About Term Document Incidence Matrix Part No 1
- Term-Document Incidence Matrix (2)5:53
In This Video We Describe About Term Document Incidence Matrix Part No 2
- Inverted Index17:14
In This Video We Talk About Inverted Index
- Tradeoffs in implementing an Inverted Index13:07
In This Video We Talk About Tradeoffs In Implementing An Inverted Index
- Processing AND, OR, NOT queries19:11
In this video we describe about Processing AND OR NOT Queries
- Overview of Index Construction Pipeline19:10
In This Video We Describe About Overview Of Index Construction Pipeline
- Query optimization using Document Frequency (1)9:54
In This Video We Describe About Query Optimization Using Document Frequency 1
- Query Optimization Using Document Frequency (2)11:28
In This Video We Talk About Query Optimization Using Document Frequency 2
- Boolean Retrieval Model12:22
In This Video We Describe About Boolean Retrieval Model
- Example of a Boolean Retrieval Model16:00
In This Video We Describe About Example Of A Boolean Retrieval Model
- Limitations of Boolean Retrieval Model6:53
In this video we describe about Limitations Of A Boolean Retrieval Model
- How to evaluate performance of an IR System9:39
In this video we talk about How To Evaluate Performance Of An IR System
- Google zeitgeist5:28
- Parsing Documents and Issues Associated with it32:57
In this video we talk about Parsing Document
- Tokenization Process in an IR System47:03
In this video we describe about Tokenization process in an ir system
- Normalization to Terms59:32
In this video we say about Normalization To Terms
- Faster Postings Merges With Skip Pointers15:01
In This Video We Describe About Faster Postings Merges With Skip Pointers
- How to Handle Phrase Query12:50
In this video we describe about How To Handle Phrase Query
- Phrase Query Using Positional Index20:02
In this video we describe about Phrase Query Using Positional Index
- How to handle proximity query3:18
In this video we say about How To Handle Proximity Query
- Discussion on Positional Index Size11:20
In this video we talk about Discussion On Positional Index Size
- Dictionary Data Structure Implementation36:03
In this video we describe about Dictionary Data Structure Implementation
- Wild card queries9:02
In this video we describe about Wild Card Queries
- Questions on Wild Card Queries17:07
In this video we describe about Question On Wild Card Queries
- Wild Card Query Handling Using Permuterm Index29:25
In this video we talk about Wild Card Query Handling Using Permuterm Index
- Wild Card Query Handling Using K-Gram Index16:13
In this video we talk about Wild Card Query Handling Using K-Gram Index
- Soundex Algorithm11:47
In this video we talk about Soundex Algorithm
- Spelling Correction Techniques in an IR System10:42
In this video we describe about Spelling Correction
- Question On Soundex Algorithm8:35
In this video we describe about Question On Soundex Algorithm
- Spelling Correction (Part 2)17:52
In this video we describe about Spelling Correction Intro
- Introduction To Dynamic Programming9:47
In this video we talk about Intro To Dynamic Programming
- How To Calculate Edit Distance Between Two Strings1:03:13
In this video we talk about How To Calculate Edit Distance Between Two Strings
- Spelling Correction Using Weighted Edit Distance24:13
In this video we describe about Spelling Correction Using Weighted Edit Distance
- Spelling Correction Using Ngram Overlap Technique21:34
In this video we describe about Spelling Correction Using Ngram Overlap Technique
- Calculating Jaccard Coefficient (An Example)34:38
In this video we say about Calculating Jaccard Coefficient
- Context Sensitive Spell Correction12:42
In this video we talk about Context Sensitive Spell Correction
- Introduction to Index Construction29:30
In this video we talk about Index Construction Introduction
- Index Construction Using InMemory Sorting13:10
In this video we describe Index Construction Using In Memory Sorting
- Index Construction Using BSBI Algorithm43:33
In this video we talk about Index Construction Using BSBI Algorithm
- Index Construction Using SPIMI Algorithm12:32
In this video we talk about Index Construction Using SPIMI Algorithm
- Introduction To Distributed Indexing10:38
In this video we describe about Introduction To Distributed Indexing
- How To build distributed indexes25:02
In this video we describe about How To Build Distributed Indexes
- Q & A on Distributed Index10:00
In this video we talk about QA On Distributed Index
- Map Reduce24:19
In this video we say about Map Reduce
- Dynamic indexing using naive approach11:03
In this video we talk about Dynamic Indexing Using Naive Approach
- Dynamic indexing using logarithimic merge17:02
In this video we talk about Dynamic Indexing Using Logarithimic Merge
- Issues With Multiple Indexes2:57
In this video we describe about Issues With Multiple Indexes
- Why do we compress indexes7:09
In this video we describe about Why Compression Indexes
- Important Statistics about RCV Collection41:41
In this video we talk about RCV Collection Statistics
- Various Dictionary Compression Techniques31:44
In this video we describe about Dictionary Compression Technique
- Various Dictionary Compression Techniques Part 235:07
In this video we say about Various Dictionary Compression Techniques Part 2
- Various Posting Compression Techniques59:10
In this video we describe about Various Posting Compression Techniques
- Ranked Retrieval Model13:30
In this video we talk about Ranked Retrieval Model
- Jaccard Score14:05
In this video we describe about Jaccard Score
- Term Frequency Weighing And Bag Of Words Model24:39
In this video we describe about Term Frequency Weighing And Bag of Words Model
- Inverse Document Frequency16:42
In this video we talk about Inverse Document Frequency
- TF-IDF Score12:14
In this video we describe about TF IDF Score
- Documents AS TF-IDF Vectors9:57
In this video we describe about Documents As TF IDF Vectors
- Length Normalization28:49
In this video we talk about Length Normalization
- Cosine Similarity Example24:41
In this video we describe about Cosine Similarity Example
- Computing Cosine Scores On Index17:51
In this video we tell about Computing Cosine Scores On Index
- Variants of TF IDF Weights4:03
In this video we describe about Variants Of TF IDF Weight
- Term at a Time Scoring14:59
In this video we describe about Term At A Time Scoring
- Efficient Cosine Ranking14:51
In this video we say about Efficient Cosine Ranking
- Generic Approach For Speeding up Cosine Similarity5:48
In this video we describe about Generic Approach For Speeding Up Cosine Similarity
- Index Elimination15:19
In this video we describe about Index Elimination
- Champion Lists7:48
In this video we talk about Champion Lists
- Static Quality Score18:56
In this video we describe about Static Quality Score
- High And Low Lists2:16
In this video we talk about High And Low Lists
- Impact Ordered Posting7:38
In this video we say about Impact Ordered Posting
- Cluster Pruning18:33
In this video we describe about Cluster Pruning
- Parametric Zone Tired Index20:46
In this video we describe about Parametric Zone Tired Inde
- Query Term Proximity And Query Parsing14:35
In this video we say about Query Term Proximity And Query Parsing
- How A Search Engine Works9:49
In this video we describe about How A Search Engine Works
- Performance of a Search Engine Part 116:53
In this video we say about Search Engine Evaluation Part
- Performance of a Search Engine Part 222:50
In this video we describe about Search Engine Evaluation Part 2
- Performance of a Search Engine Part 310:44
In this video we describe about Search Engine Evaluation Part 3
- Performance of a Search Engine Part 416:27
In this video we describe about Search Engine Evaluation Part 4
- Performance of a Search Engine Part 524:34
In this video we say about Search Engine Evaluation Part 5
- ECommerce Vs. Traditional Businesses10:21
In this video we describe about ECommerce Vs. Traditional Businesses
- Pricing Models For Online Advertisement39:44
In this video we describe about Pricing Models For Online Advertisement
- AdWords and AdSense32:39
In this video we describe about Ad Words Ad Sense
- SEM And SEO17:57
In this video we describe about SEM And SEO
- Classification System20:29
In this video we describe about Introduction To Classification
- Document Classification7:53
In this video we talk about Document Classification
- Manual Classification Methods4:54
In this video we describe about Manual Classification Methods
- Naive Bayes Classifiers56:18
In this video we talk about Naive Bayes Classifiers
- Bayes Rules Of Text Classification17:23
In this video we talk about Bayes Rule Of Text Classification
- Various Classification Methods3:27
In this video we describe about Classification Methods
- Example of Multivariate Bernouli Model27:33
In this video we describe about Example Of Multivariate Bernouli Model
- Second Version of Naive Bayes10:37
In this video we describe about Second Version Of Naive Bayes
- Example of Second Version of Naive Bayes20:41
In this video we talk about Workedout Example Of Second Version of Naive Bayes
Requirements
- Knowledge of probability and linear algebra.
- Good grasp on graduate level algorithms.
- Experience with a programming language ( C, Python, Java)
Description
The goal is to introduce various techniques required to build an IR System. In this course we will explore various methods to solve big data problem. We will evaluate alternative solutions and trade offs. In the later part of the course we will discuss various data mining algorithms to make sense of massive data sets.
Who this course is for:
- Big Data Enthusiast
- Data Scientists