Information Retrieval and Mining Massive Data Sets

Information Retrieval and Mining Massive Data Sets, available at $69.99, has an average rating of 4.25, with 123 lectures, based on 123 reviews, and has 2133 subscribers.

You will learn about 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 This course is ideal for individuals who are Big Data Enthusiast or Data Scientists It is particularly useful for Big Data Enthusiast or Data Scientists.

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Summary

Title: Information Retrieval and Mining Massive Data Sets

Price: $69.99

Average Rating: 4.25

Number of Lectures: 123

Number of Published Lectures: 123

Number of Curriculum Items: 123

Number of Published Curriculum Objects: 123

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Big Data Enthusiast

Data Scientists

Target Audiences

Big Data Enthusiast

Data Scientists

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.

Course Curriculum

Chapter 1: Introduction To a Boolean Search Engine

Lecture 1: What is Data Mining

Lecture 2: Structured Data, Unstructured data and Information Retrieval

Lecture 3: Term-Document Incidence Matrix (1)

Lecture 4: Term-Document Incidence Matrix (2)

Lecture 5: Inverted Index

Lecture 6: Tradeoffs in implementing an Inverted Index

Lecture 7: Processing AND, OR, NOT queries

Lecture 8: Overview of Index Construction Pipeline

Lecture 9: Query optimization using Document Frequency (1)

Lecture 10: Query Optimization Using Document Frequency (2)

Lecture 11: Boolean Retrieval Model

Lecture 12: Example of a Boolean Retrieval Model

Lecture 13: Limitations of Boolean Retrieval Model

Lecture 14: How to evaluate performance of an IR System

Lecture 15: Google zeitgeist

Chapter 2: Dictionary Data Structure. Tolerant retrieval

Lecture 1: Parsing Documents and Issues Associated with it

Lecture 2: Tokenization Process in an IR System

Lecture 3: Normalization to Terms

Lecture 4: Faster Postings Merges With Skip Pointers

Lecture 5: How to Handle Phrase Query

Lecture 6: Phrase Query Using Positional Index

Lecture 7: How to handle proximity query

Lecture 8: Discussion on Positional Index Size

Chapter 3: Index construction. Postings size estimation, sort-based indexing, dynamic index

Lecture 1: Dictionary Data Structure Implementation

Lecture 2: Wild card queries

Lecture 3: Questions on Wild Card Queries

Lecture 4: Wild Card Query Handling Using Permuterm Index

Lecture 5: Wild Card Query Handling Using K-Gram Index

Lecture 6: Soundex Algorithm

Lecture 7: Spelling Correction Techniques in an IR System

Lecture 8: Question On Soundex Algorithm

Lecture 9: Spelling Correction (Part 2)

Lecture 10: Introduction To Dynamic Programming

Lecture 11: How To Calculate Edit Distance Between Two Strings

Lecture 12: Spelling Correction Using Weighted Edit Distance

Lecture 13: Spelling Correction Using Ngram Overlap Technique

Lecture 14: Calculating Jaccard Coefficient (An Example)

Lecture 15: Context Sensitive Spell Correction

Chapter 4: Dictionary Compression, Posting Compression

Lecture 1: Introduction to Index Construction

Lecture 2: Index Construction Using InMemory Sorting

Lecture 3: Index Construction Using BSBI Algorithm

Lecture 4: Index Construction Using SPIMI Algorithm

Lecture 5: Introduction To Distributed Indexing

Lecture 6: How To build distributed indexes

Lecture 7: Q & A on Distributed Index

Lecture 8: Map Reduce

Lecture 9: Dynamic indexing using naive approach

Lecture 10: Dynamic indexing using logarithimic merge

Lecture 11: Issues With Multiple Indexes

Chapter 5: Scoring, term weighting, and the vector space model

Lecture 1: Why do we compress indexes

Lecture 2: Important Statistics about RCV Collection

Lecture 3: Various Dictionary Compression Techniques

Lecture 4: Various Dictionary Compression Techniques Part 2

Lecture 5: Various Posting Compression Techniques

Chapter 6: Efficient vector space scoring. Nearest neighbor techniques

Lecture 1: Ranked Retrieval Model

Lecture 2: Jaccard Score

Lecture 3: Term Frequency Weighing And Bag Of Words Model

Lecture 4: Inverse Document Frequency

Lecture 5: TF-IDF Score

Lecture 6: Documents AS TF-IDF Vectors

Lecture 7: Length Normalization

Lecture 8: Cosine Similarity Example

Lecture 9: Computing Cosine Scores On Index

Lecture 10: Variants of TF IDF Weights

Chapter 7: Evaluating search engines. User happiness, precision, recall, F-measure

Lecture 1: Term at a Time Scoring

Lecture 2: Efficient Cosine Ranking

Lecture 3: Generic Approach For Speeding up Cosine Similarity

Lecture 4: Index Elimination

Lecture 5: Champion Lists

Lecture 6: Static Quality Score

Lecture 7: High And Low Lists

Lecture 8: Impact Ordered Posting

Lecture 9: Cluster Pruning

Lecture 10: Parametric Zone Tired Index

Lecture 11: Query Term Proximity And Query Parsing

Lecture 12: How A Search Engine Works

Chapter 8: Advertisement Systen. Google AdSense. Search Engine Optimization

Lecture 1: Performance of a Search Engine Part 1

Lecture 2: Performance of a Search Engine Part 2

Lecture 3: Performance of a Search Engine Part 3

Lecture 4: Performance of a Search Engine Part 4

Lecture 5: Performance of a Search Engine Part 5

Chapter 9: Supervised Learning. Text Classification. Naive-Bayes Text Classification

Lecture 1: ECommerce Vs. Traditional Businesses

Lecture 2: Pricing Models For Online Advertisement

Lecture 3: AdWords and AdSense

Lecture 4: SEM And SEO

Chapter 10: Link analysis. Web as a graph. PageRank

Lecture 1: Classification System

Lecture 2: Document Classification

Lecture 3: Manual Classification Methods

Lecture 4: Naive Bayes Classifiers

Lecture 5: Bayes Rules Of Text Classification

Instructors

Omkar Deshpande
Principal Engineer at WalmartLabs

Mentors Net
Touch More Lives, Impart More Wisdom

Rating Distribution

1 stars: 4 votes

2 stars: 3 votes

3 stars: 17 votes

4 stars: 37 votes

5 stars: 62 votes

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