[Packt] Big Data Analytics Projects with Apache Spark [FCO] GloDLS
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Torrent File Contents
[Packt] Big Data Analytics Projects with Apache Spark [FCO] GloDLS
1.Finding Top Selling Product/01.The Course Overview.mp4
1.Finding Top Selling Product/02.Explaining Ways of Joining Datasets.mp4
1.Finding Top Selling Product/03.Developing Spark Algorithm for Joining_Windowing Datasets.mp4
1.Finding Top Selling Product/04.Testing Logic in MapReduce Spark — Finding Top Sellers.mp4
1.Finding Top Selling Product/05.Drawing Conclusions from Top Sellers Data.mp4
2.Market Basket Analysis/06.Market Basket Analysis Goals.mp4
2.Market Basket Analysis/07.Where MBA Algorithms Are Useful.mp4
2.Market Basket Analysis/08.Implementing MBA MapReduce Algorithm in Spark.mp4
2.Market Basket Analysis/09.Finding Association Rules Between Products.mp4
3.Finding an Author Using Probabilistic Logistic Regression/10.Analyzing Post for an Author.mp4
3.Finding an Author Using Probabilistic Logistic Regression/11.Extracting Information from Unstructured Text.mp4
3.Finding an Author Using Probabilistic Logistic Regression/12.Extracting Information via Spark DataFrame.mp4
3.Finding an Author Using Probabilistic Logistic Regression/13.Sentiment Analysis of Posts Using Logistic Regression.mp4
3.Finding an Author Using Probabilistic Logistic Regression/14.Finding an Author of a Post.mp4
4.Content-Based Recommendation System - Movies/15.Content-Based Recommendation Systems Explanation.mp4
4.Content-Based Recommendation System - Movies/16.Finding Correlation Between Movies and Users.mp4
4.Content-Based Recommendation System - Movies/17.Testing Logic in MapReduce Spark.mp4
4.Content-Based Recommendation System - Movies/18.Finding Recommendation for Given User.mp4
5.Social Network Friend Recommendation/19.Finding Common Friends Problem — Graph Approach.mp4
5.Social Network Friend Recommendation/20.Creating a Graph Using GraphX and Property Graph.mp4
5.Social Network Friend Recommendation/21.Solution — Examining Available Methods.mp4
5.Social Network Friend Recommendation/22.Finding Closest Friend for Given User Using Page Rank.mp4
Exercise Files/code_33899.zip
Discuss.FTUForum.com.html
FreeCoursesOnline.Me.html
FTUForum.com.html
How you can help Team-FTU.txt
[TGx]Downloaded from torrentgalaxy.org.txt
Torrent Downloaded From GloDls.buzz.txt
1.Finding Top Selling Product/01.The Course Overview.mp4
- 21.36 MB
1.Finding Top Selling Product/02.Explaining Ways of Joining Datasets.mp4
- 73.26 MB
1.Finding Top Selling Product/03.Developing Spark Algorithm for Joining_Windowing Datasets.mp4
- 40.01 MB
1.Finding Top Selling Product/04.Testing Logic in MapReduce Spark — Finding Top Sellers.mp4
- 14.31 MB
1.Finding Top Selling Product/05.Drawing Conclusions from Top Sellers Data.mp4
- 24.3 MB
2.Market Basket Analysis/06.Market Basket Analysis Goals.mp4
- 36.75 MB
2.Market Basket Analysis/07.Where MBA Algorithms Are Useful.mp4
- 32.63 MB
2.Market Basket Analysis/08.Implementing MBA MapReduce Algorithm in Spark.mp4
- 28.9 MB
2.Market Basket Analysis/09.Finding Association Rules Between Products.mp4
- 24.48 MB
3.Finding an Author Using Probabilistic Logistic Regression/10.Analyzing Post for an Author.mp4
- 9.46 MB
3.Finding an Author Using Probabilistic Logistic Regression/11.Extracting Information from Unstructured Text.mp4
- 16.51 MB
3.Finding an Author Using Probabilistic Logistic Regression/12.Extracting Information via Spark DataFrame.mp4
- 19.34 MB
3.Finding an Author Using Probabilistic Logistic Regression/13.Sentiment Analysis of Posts Using Logistic Regression.mp4
- 19.43 MB
3.Finding an Author Using Probabilistic Logistic Regression/14.Finding an Author of a Post.mp4
- 10.82 MB
4.Content-Based Recommendation System - Movies/15.Content-Based Recommendation Systems Explanation.mp4
- 10.96 MB
4.Content-Based Recommendation System - Movies/16.Finding Correlation Between Movies and Users.mp4
- 15.83 MB
4.Content-Based Recommendation System - Movies/17.Testing Logic in MapReduce Spark.mp4
- 28.62 MB
4.Content-Based Recommendation System - Movies/18.Finding Recommendation for Given User.mp4
- 19.61 MB
5.Social Network Friend Recommendation/19.Finding Common Friends Problem — Graph Approach.mp4
- 7.44 MB
5.Social Network Friend Recommendation/20.Creating a Graph Using GraphX and Property Graph.mp4
- 34.97 MB
5.Social Network Friend Recommendation/21.Solution — Examining Available Methods.mp4
- 25.35 MB
5.Social Network Friend Recommendation/22.Finding Closest Friend for Given User Using Page Rank.mp4
- 117.96 MB
Exercise Files/code_33899.zip
- 2 MB
Discuss.FTUForum.com.html
- 31.89 KB
FreeCoursesOnline.Me.html
- 108.3 KB
FTUForum.com.html
- 100.44 KB
How you can help Team-FTU.txt
- 235 Bytes
[TGx]Downloaded from torrentgalaxy.org.txt
- 524 Bytes
Torrent Downloaded From GloDls.buzz.txt
- 84 Bytes
Torrent Description
Description:
By: Tomasz Lelek
Released: Monday, June 25, 2018
Torrent Contains: 29 Files, 6 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/big-data-analytics-projects-apache-spark-video
Perform real-life data operations with Apache Spark.
Video Details
ISBN 9781789132373
Course Length 2 hour 4 minutes
Table of Contents
• FINDING TOP SELLING PRODUCT
• MARKET BASKET ANALYSIS
• FINDING AN AUTHOR USING PROBABILISTIC LOGISTIC REGRESSION
• CONTENT-BASED RECOMMENDATION SYSTEM: MOVIES
• SOCIAL NETWORK FRIEND RECOMMENDATION
Video Description
Ready to use statistical and machine-learning techniques across large data sets? This course shows you how the Apache Spark and the Hadoop MapReduce ecosystem is perfect for the job.
This course contains various projects that consist of real-world examples. The first project is to find top selling products for an e-commerce business by efficiently joining data sets in the Map/Reduce paradigm. Next, a Market Basket Analysis will help you identify items likely to be purchased together and find correlations between items in a set of transactions.
Moving on, you'll learn about probabilistic logistic regression by finding an author for a post. Next, you'll build a content-based recommendation system for movies to predict whether an action will happen, which we’ll do by building a trained model. Finally, we’ll use the Map/Reduce Spark program to calculate mutual friends on social network.
By the end of this course, you’ll have been exposed to a wide variety of mathematical techniques that can be utilized as training models with the Spark and Hadoop software, and know how to solve common problems.
Style and Approach
This will help you perform data analysis, introducing to each subject by example and practice that makes the audience more productive after each video.
What You Will Learn
• Learn See how to process big data effectively
• Examine a number of real-world use cases and hands-on code examples.
• Build Hadoop and Apache Spark jobs that process data quickly and effectively.
• Write programs for complex data analysis and solving to solve real real-world problems
• Explore the Map/Reduce Hadoop and Spark approach for solvinto solveg data analysis problems.
Authors
Tomasz Lelek
Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. He has worked with ML algorithms for the past 5 years, with production experience in processing petabytes of data.
He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and also at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.
He is a co-founder of www.initlearn.com, an e-learning platform that was built with the Java language. He has also written articles about everything related to the Java world: http://www.baeldung.com/.
By: Tomasz Lelek
Released: Monday, June 25, 2018
Torrent Contains: 29 Files, 6 Folders
Course Source: https://www.packtpub.com/big-data-and-business-intelligence/big-data-analytics-projects-apache-spark-video
Perform real-life data operations with Apache Spark.
Video Details
ISBN 9781789132373
Course Length 2 hour 4 minutes
Table of Contents
• FINDING TOP SELLING PRODUCT
• MARKET BASKET ANALYSIS
• FINDING AN AUTHOR USING PROBABILISTIC LOGISTIC REGRESSION
• CONTENT-BASED RECOMMENDATION SYSTEM: MOVIES
• SOCIAL NETWORK FRIEND RECOMMENDATION
Video Description
Ready to use statistical and machine-learning techniques across large data sets? This course shows you how the Apache Spark and the Hadoop MapReduce ecosystem is perfect for the job.
This course contains various projects that consist of real-world examples. The first project is to find top selling products for an e-commerce business by efficiently joining data sets in the Map/Reduce paradigm. Next, a Market Basket Analysis will help you identify items likely to be purchased together and find correlations between items in a set of transactions.
Moving on, you'll learn about probabilistic logistic regression by finding an author for a post. Next, you'll build a content-based recommendation system for movies to predict whether an action will happen, which we’ll do by building a trained model. Finally, we’ll use the Map/Reduce Spark program to calculate mutual friends on social network.
By the end of this course, you’ll have been exposed to a wide variety of mathematical techniques that can be utilized as training models with the Spark and Hadoop software, and know how to solve common problems.
Style and Approach
This will help you perform data analysis, introducing to each subject by example and practice that makes the audience more productive after each video.
What You Will Learn
• Learn See how to process big data effectively
• Examine a number of real-world use cases and hands-on code examples.
• Build Hadoop and Apache Spark jobs that process data quickly and effectively.
• Write programs for complex data analysis and solving to solve real real-world problems
• Explore the Map/Reduce Hadoop and Spark approach for solvinto solveg data analysis problems.
Authors
Tomasz Lelek
Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. He has worked with ML algorithms for the past 5 years, with production experience in processing petabytes of data.
He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and also at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.
He is a co-founder of www.initlearn.com, an e-learning platform that was built with the Java language. He has also written articles about everything related to the Java world: http://www.baeldung.com/.