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Udemy

Information Retrieval and Mining Massive Data Sets

via Udemy

Overview

Learn various techniques to build a Google scale Information Retrieval System.

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

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.

Syllabus

  • Introduction To a Boolean Search Engine
  • Dictionary Data Structure. Tolerant retrieval
  • Index construction. Postings size estimation, sort-based indexing, dynamic index
  • Dictionary Compression, Posting Compression
  • Scoring, term weighting, and the vector space model
  • Efficient vector space scoring. Nearest neighbor techniques
  • Evaluating search engines. User happiness, precision, recall, F-measure
  • Advertisement Systen. Google AdSense. Search Engine Optimization
  • Supervised Learning. Text Classification. Naive-Bayes Text Classification
  • Link analysis. Web as a graph. PageRank
  • Clustering. Introduction to the problem. Partitioning methods: k-means clusterin
  • Web Crawler
  • Association Rules. Market Basket Model and Frequent Item Sets. A Priori Algorith
  • Association Rules. Market Basket Model and Frequent Item Sets. A Priori Algorith

Taught by

Omkar Deshpande and Mentors Net

Reviews

4.4 rating at Udemy based on 135 ratings

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