Choose dataset. The Posts.
From 0.9 to 1.0
The file size is This xml file contains the stackoverflow. The full dataset with stackoverflow. This file contains a small size of original dataset. This data is licensed under the Creative Commons license cc-by-sa. As you might expect, this small file is not the best choice for model training. This file is only good for experimenting with your data preparation code.
However, the end-to-end Spark scenario from this article works with this small file as well.
Machine Learning Library (MLlib) - Spark Documentation
Please download the file from here. Our goal is to create a predictive model which predicts post Tags based on Body and Title. To simplify the task and reduce the amount of code, we are going to concatenate Title and Body and use that as a single text column. It might be easy to imagine how this model should work in the stackoverflow.
Assume that we need as many correct tags as possible and that the user would remove the unnecessary tags. Because of this assumption we are choosing recall as a high priority target for our model. Binary and multi-label classification The problem of stackoverflow tag prediction is a multi-label classification one because the model should predict many classes, which are not exclusive. Note that multi-label classification is a generalization of different problems — multi-class classification problem which predict only one class from a set of classes.
This approach is simple and good for studying.
An Introduction To Machine Learning Using Spark Language
Apache Spark is an open source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost effective manner. Therefore an applied knowledge of working with Apache Spark is a great asset and potential differentiator for a Machine Learning engineer.
After completing this course, you will be able to: - gain a practical understanding of Apache Spark, and apply it to solve machine learning problems involving both small and big data - understand how parallel code is written, capable of running on thousands of CPUs. NOTE: You will practice running machine learning tasks hands-on on an Apache Spark cluster provided by IBM at no charge during the course which you can continue to use afterwards. This is an introduction to Apache Spark. You'll learn how Apache Spark internally works and how to use it for data processing.
Then, different types of data storage solutions are contrasted. Understand the concept of machine learning pipelines in order to understand how Apache SparkML works programmatically.
Machine Learning with Spark MLlib
Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments. When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
If you only want to read and view the course content, you can audit the course for free. More questions? Visit the Learner Help Center. Browse Chevron Right.
Installing Apache Spark
Data Science Chevron Right. Machine Learning. Offered By. About this Course 26, recent views. Flexible deadlines.
Flexible deadlines Reset deadlines in accordance to your schedule. Intermediate Level.
Hours to complete. Available languages. English Subtitles: English. Chevron Left. Syllabus - What you will learn from this course. Video 6 videos. What is Big Data? Data storage solutions 5m. Parallel data processing strategies of Apache Spark 7m.
Functional programming basics 6m. Reading 5 readings. Course Syllabus 10m. Setup of the grading and exercise environment 10m. Exercise 1 - working with RDD 10m.
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