Packt Publishing – Spark for Machine Learning [Video]
English | Size: 294.01 MB
Category: Tutorial
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.
This step-by step and practical video course will teach you how to build amazing machine learning systems using Spark.
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
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