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You're reading from  Machine Learning with Spark. - Second Edition

Product typeBook
Published inApr 2017
Reading LevelIntermediate
PublisherPackt
ISBN-139781785889936
Edition2nd Edition
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Authors (2):
Rajdeep Dua
Rajdeep Dua
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Rajdeep Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.
Read more about Rajdeep Dua

Manpreet Singh Ghotra
Manpreet Singh Ghotra
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Manpreet Singh Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.
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Types of machine learning models

While we have one example, there are many other examples, some of which we will touch on in the relevant chapters when we introduce each machine learning task.

However, we can broadly divide the preceding use cases and methods into two categories of machine learning:

  • Supervised learning: These types of models use labeled data to learn. Recommendation engines, regression, and classification are examples of supervised learning methods. The labels in these models can be user--movie ratings (for the recommendation), movie tags (in the case of the preceding classification example), or revenue figures (for regression). We will cover supervised learning models in Chapter 4, Building a Recommendation Engine with Spark, Chapter 6, Building a Classification Model with Spark, and Chapter 7, Building a Regression Model with Spark.
  • Unsupervised learning: When a model does not require labeled...
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Machine Learning with Spark. - Second Edition
Published in: Apr 2017Publisher: PacktISBN-13: 9781785889936

Authors (2)

author image
Rajdeep Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.
Read more about Rajdeep Dua

author image
Manpreet Singh Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.
Read more about Manpreet Singh Ghotra