About This Video
- Leverage Spark to make your machine learning processing distributed and much faster compared to a standard machine learning toolkit like R or Python
- Use Natural Language Processing techniques to create a program that learns structure of the posts in a forum
- Use Gaussian Mixture Model and Logistic Regression from MLlib
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.
Table of contents
- Chapter 1 : Advanced Text Processing and Building Classification Model
- Chapter 2 : Building a Regression Model with Spark
- Chapter 3 : Building a Clustering Model with Spark
Chapter 4 : Dimensionality Reductions and Recommendation Engines
- Dimensionality Reduction 00:03:30
- Building Recommendation Engine 00:02:12
- Using Recommendation Engine to Get TOP Recommendations 00:10:25
- Chapter 5 : Graph Processing with GraphX
- Title: Spark for Machine Learning
- Release date: September 2017
- Publisher(s): Packt Publishing
- ISBN: 9781786466594
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