Hands-On Machine Learning Recommender Systems with Apache Spark

Length: 107 pages
Edition: 1
Language: English
Publisher: Consultants Network
Publication Date: 2020-04-13
ISBN-10: B08WPPC8WL
Sales Rank: #826188 (See Top 100 Books)

This book is intended to provide an introduction to recommender systems using Apache Spark and Machine Learning. Before we begin with recommender systems using Apache Spark, we define Big Data and Machine Learning. We then dive directly into our use case of building a recommender system with Apache Spark and Machine learning by showing you how to build a recommender system – step by step.

CHAPTER 1: INTRODUCTION TO BIG DATA & AI
    Theory
An Overview of Big Data
    Quick Introduction to Hadoop
        Why Hadoop?
    Quick Introduction to Hadoop Distributed File System
        Block Placement in HDFS
        HDFS Architecture
    Introduction to MapReduce
        Architecture of MapReduce
        Processing Data with MapReduce
    3V’s of Hadoop
    Introduction to Spark
        What is Spark?
        Why Spark?
        Components of Spark
        Introduction to RDD
        Architecture of Spark
        Job Workflow in Spark
    Introduction to SparkSQL
        What is Spark SQL?
        Why Spark SQL?
        Spark SQL Architecture
        What are Datasets?
        Why Datasets?
        Spark Data Storage
        Various Spark Versions
    Introduction to Artificial Intelligence
        What is AI?
        What is Machine Learning?
        What is Deep Learning?
AIM
LAB EXERCISE 1: Installations
    Task 1: Download and Install JDK
    Task 2: Download and Install Scala
    Task 3: Download and Install Spark
    Task 4: Download and Install IntelliJ IDEA
    Task 5: Configuring IntelliJ IDEA
SUMMARY
REFERENCES
CHAPTER 2: RECOMMENDER SYSTEMS USING APACHE SPARK
    Theory
    Basics of Machine Learning
        Machine Learning Techniques
    Getting Started with Recommender Systems
        What is Recommender System?
        Why Recommender Systems?
        Recommender Systems Techniques
            Content-Based Filtering
            Collaborative Filtering
    Spark MLlib Overview
        Spark MLlib Components
            Pipeline
    Alternating Least Squares
AIM
LAB EXERCISE 2: Movie Recommender System using Apache Spark
    Task 1: Download MovieLens dataset
    Task 2: Creating a new package in IntelliJ IDEA
    Task 3: Mapping Movie Ids with Movie Names
    Task 4: Loading and Mapping User Data
    Task 5: Training Recommendation Model
    Task 6: Running the Model
REFERENCES