Spark for Machine Learning [Video]

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Spark for Machine Learning [Video]

Tomasz Lelek

Build mindblowing machine learning systems with Apache Spark
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Video Details

ISBN 139781786466594
Course Length1 hour 25 minutes

Video Description

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.

Style and Approach

This step-by step and practical video course will teach you how to build amazing machine learning systems using Spark.

Table of Contents

Advanced Text Processing and Building Classification Model
The Course Overview
Analyzing Text Input Data
Extracting Features from Data
Implementing Word2Vect Using Apache Spark
Building a Regression Model with Spark
Logistic Regression Explanation
Writing Logistic Regression Model per Author
Validate Models Using Cross-Validation
Building a Clustering Model with Spark
Analyzing Time of Post Using Clustering - GMM Explanation
Implementing GMM in Apache Spark
Measuring Accuracy Using Area Under ROC
Dimensionality Reductions and Recommendation Engines
Dimensionality Reduction
Building Recommendation Engine
Using Recommendation Engine to Get TOP Recommendations
Graph Processing with GraphX
What is a Graph?
GraphX API
Structural Operations on Graph
Neighborhood Aggregation

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

Authors

Table of Contents

Advanced Text Processing and Building Classification Model
The Course Overview
Analyzing Text Input Data
Extracting Features from Data
Implementing Word2Vect Using Apache Spark
Building a Regression Model with Spark
Logistic Regression Explanation
Writing Logistic Regression Model per Author
Validate Models Using Cross-Validation
Building a Clustering Model with Spark
Analyzing Time of Post Using Clustering - GMM Explanation
Implementing GMM in Apache Spark
Measuring Accuracy Using Area Under ROC
Dimensionality Reductions and Recommendation Engines
Dimensionality Reduction
Building Recommendation Engine
Using Recommendation Engine to Get TOP Recommendations
Graph Processing with GraphX
What is a Graph?
GraphX API
Structural Operations on Graph
Neighborhood Aggregation

Video Details

ISBN 139781786466594
Course Length1 hour 25 minutes
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