Articles

Real-Time Machine Learning With TensorFlow in Data Collector

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The real value of a modern DataOps platform is realized only when business users and applications are able to access raw and aggregated data from a range of sources, and produce data-driven insights in a timely manner. And with Machine Learning (ML), analysts and data scientists can leverage historical data to help make better, data-driven business decisions-offline and in real-time using technologies such as TensorFlow.

In this post, you will learn how to use TensorFlow (TF) models for prediction and classification using the newly released TensorFlow Evaluator* in StreamSets Data Collector 3.5.0 and StreamSets Data Collector Edge.

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What You Must Know Before You Dive Into Machine Learning

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Machine learning refers to the process of enabling computer systems to learn with data using statistical techniques without being explicitly programmed. It is the process of active engagement with algorithms in order to enable them to learn from and make predictions on data. Machine learning is closely associated with computational statistics, mathematical optimization, and data learning. It is associated with predictive analysis, which allows producing reliable and fast results by learning from historical trends. There are basically two kinds of machine learning tasks:

  1. Supervised learning: The computer is presented with some example inputs, based on which the desired outputs are to be formed. The computer is made to learn general rules of converting inputs to outputs.

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Machine Learning and Artificial Intelligence

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Machine Learning and Artificial Intelligence

The difference between Machine Learning and Artificial Intelligence: "Okay Google! What’s Up? Could you play my favorite track or Book a Cab from Palace Road to MG Road."

"Alexa, What time it is?" "Wake me up at 5 am." "Could you please tell me my tomorrow meetings?"


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Data Science vs. AI vs. ML vs. Deep Learning

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Comparison Between Data Science, AI, ML, and Deep Learning

What Is Data Science?

R Data science includes data analysis. It is an important component of the skill set required for many jobs in this area. But it’s not the only necessary skill. They play active roles in the design and implementation work of four related areas:

  • Data architecture
  • In data acquisition
  • Data analysis
  • In data archiving

Learn more about Data Science.


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Python Machine Learning: Tasks and Applications

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Python and Machine Learning

In this article, we will introduce you to Machine Learning with Python. Moreover, we will discuss Python Machine Learning tasks, steps, and applications. Then, we will take a look at 10 tech giants that adopt Python Machine Learning to improve what they do.

So, let’s begin!


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Start NLP with a Single Neuron

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What Is a Classification Problem?

Classification is an important and central topic in ML, which has to do with training machines how to group together data by particular criteria. Classification is the process where computers group data together based on predetermined characteristics — this is called supervised learning. There is an unsupervised version of classification, called clustering where computers find shared characteristics by which to group data when categories are not specified.

For example:


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