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Collective Intelligence

  • Course Code: Data Science - Collective Intelligence
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to develop software that you can embed in your own applications.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Collective Intelligence skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to develop software that you can embed in your own applications. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

In the Web 2.0 era, leveraging the collective power of user contributions, interactions, and feedback is the key to market dominance. A new category of powerful programming techniques lets you discover the patterns, inter-relationships, and individual profiles—the collective intelligence—locked in the data people leave behind as they surf websites, post blogs, and interact with other users. 

Collective Intelligence is a course for implementing collective-intelligence concepts using Java. It is the first Java-based course to emphasize the underlying algorithms and technical implementation of vital data gathering and mining techniques like analyzing trends, discovering relationships, and making predictions. It provides a pragmatic approach to personalization by combining content-based analysis with collaborative approaches. 

Working in a hands-on learning environment, led by our Collective Intelligence expert instructor, students will learn about and explore: 

  • Architecture for embedding intelligence in your application 
  • Developing metadata about the user and content 
  • Gather intelligence from tagging and build tag clouds 
  • Introduction to intelligent web crawling and Nutch 
  • Harvesting information from the blogosphere 
  • Build a text analysis toolkit leveraging Lucene 
  • Business intelligence and data mining for recommendations and promotions 
  • Leveraging open-source data mining toolkit WEKA and the Java Data Mining (JDM) standard 
  • Incorporating intelligent search in your application 
  • Building a recommendation engine—finding related users and content 
  • Real-world case studies of Amazon, Google News, and Netflix personalization. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understanding collective intelligence free 
  • Learning from user interactions free 
  • Extracting intelligence from tags 
  • Extracting intelligence from content 
  • Searching the blogosphere 
  • Intelligent web crawling 
  • Data mining: process, toolkits, and standards 
  • Building a text analysis toolkit 
  • Discovering patterns with clustering 
  • Making predictions 
  • Applying intelligence in your application 
  • Intelligent search 
  • Building a recommendation engine 

Audience & Pre-Requisites 

Pre-Requisites:  Students should have familiar with  

  • you have a basic level of Java coding skills 

Course Agenda / Topics 

  1.  
  1. Understanding collective intelligencefree 
  • What is collective intelligence? 
  • CI in web applications 
  • Classifying intelligence 
  1. Learning from user interactionsfree 
  • Architecture for applying intelligence 
  • Basics of algorithms for applying CI 
  • Forms of user interaction 
  • Converting user interaction into collective intelligence 
  1. Extracting intelligence from tags 
  • Introduction to tagging 
  • How to leverage tags 
  • Extracting intelligence from user tagging: an example 
  • Scalable persistence architecture for tagging 
  • Building tag clouds 
  • Finding similar tags 
  1. Extracting intelligence from content 
  • Content types and integration 
  • The main CI-related content types 
  • Extracting intelligence step by step 
  • Simple and composite content types 
  1. Searching the blogosphere 
  • Introducing the blogosphere 
  • Building a framework to search the blogosphere 
  • Implementing the base classes 
  • Integrating Technorati 
  • Integrating Bloglines 
  • Integrating providers using RSS 
  1. Intelligent web crawling 
  • Introducing web crawling 
  • Building an intelligent crawler step by step 
  • Scalable crawling with Nutch 
  1. Data mining: process, toolkits, and standards 
  • Core concepts of data mining 
  • Using an open source data mining framework: WEKA 
  • Standard data mining API: Java Data Mining (JDM) 
  1. Building a text analysis toolkit 
  • Building the text analyzers 
  • Building the text analysis infrastructure 
  • Use cases for applying the framework 
  1. Discovering patterns with clustering 
  • Clustering blog entries 
  • Leveraging WEKA for clustering 
  • Clustering using the JDM APIs 
  1. Making predictions 
  • Classification fundamentals 
  • Classifying blog entries using WEKA APIs 
  • Regression fundamentals 
  • Regression using WEKA 
  • Classification and regression using JDM 
  1. Intelligent search 
  • Search fundamentals 
  • Indexing with Lucene 
  • Searching with Lucene 
  • Useful tools and frameworks 
  • Approaches to intelligent search 
  1. Building a recommendation engine 
  • Recommendation engine fundamentals 
  • Content-based analysis 
  • Collaborative filtering 
  • Real-world solutions 
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