English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 2h 04m | 458 MB
Perform real-life data operations with Apache Spark.
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.
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
Table of Contents
01 The Course Overview
02 Explaining Ways of Joining Datasets
03 Developing Spark Algorithm for Joining_Windowing Datasets
04 Testing Logic in MapReduce Spark — Finding Top Sellers
05 Drawing Conclusions from Top Sellers Data
06 Market Basket Analysis Goals
07 Where MBA Algorithms Are Useful
08 Implementing MBA MapReduce Algorithm in Spark
09 Finding Association Rules Between Products
10 Analyzing Post for an Author
11 Extracting Information from Unstructured Text
12 Extracting Information via Spark DataFrame
13 Sentiment Analysis of Posts Using Logistic Regression
14 Finding an Author of a Post
15 Content-Based Recommendation Systems Explanation
16 Finding Correlation Between Movies and Users
17 Testing Logic in MapReduce Spark
18 Finding Recommendation for Given User
19 Finding Common Friends Problem — Graph Approach
20 Creating a Graph Using GraphX and Property Graph
21 Solution — Examining Available Methods
22 Finding st Friend for Given User Using Page Rank
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