packtpublishing/spark-for-machine-learning

Spark for Machine Learning [Video], published by Packt

Spark for Machine Learning [Video]

This is the code repository for Spark for Machine Learning [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. Using Spark, we can create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python.

In this course, you’ll learn how to use the Spark MLlib. You’ll find out about the supervised and unsupervised ML algorithms. You’ll build classifications models, extracting proper futures from text using Word2Vect to achieve this. Next, we’ll build a Logistic Regression Model with Spark. Then we’ll find clusters and correlations in our data using K-Means clustering. We’ll learn how to validate models using cross-validation and area under the ROC measurement.

You’ll also build an effective Recommendation Model using distributed Spark algorithm. We will look at graph processing with GraphX library. By the end of the course, you’ll be able to focus on leveraging Spark to create fast and efficient machine learning programs.

What You Will Learn

• Apply Tokenization on data
• Understand Natural Language Processing techniques
• Transform text into a vector of numbers
• Implement Word2Vect in Apache Spark
• Measure accuracy on models using Spark
• Implement Logistic Regression that leverages Spark’s Distributed Processing
• Evaluate the result of trained models 
• Understand different machine learning algorithms and approaches
• Delve into graph processing using GraphX library

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
Prior working knowledge of Apache Spark.

Technical Requirements

This course has the following software requirements:
• IntelliJ IDEA
• Java JDK 8 or later
• Scala SDK
This course has been tested on the following system configuration: • OS: MacOSX • Processor: I7 2.8 • Memory: 16GB • Hard Disk Space: 200MB • Video Card: 256MB Video Memory

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