Machine Learning

Ophel Machine Learning Technologies

  • Machine learning tasks are classified into several broad categories. In supervised learning, the algorithm builds a mathematical model of a set of data that contains both the inputs and the desired outputs. For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with and without that object (the input), and each image would have a label (the output) designating whether it contained the object.
  • In special cases, the input may be only partially available, or restricted to special feedback.[clarification needed] Semi-supervised learning algorithms develop mathematical models from incomplete training data, where a portion of the sample inputs are missing the desired output.
  • Thanks to cloud-computing services, users can check their email on any computer and even store files using services such as Dropbox and Google Drive. Cloud-computing services also make it possible for users to back up their music, files and photos, ensuring that those files are immediately available in the event of a hard drive crash.

Machine Learning

Humans can typically create one or two good models a week; machine learning can create thousands of models a week.

You don’t need to come up with advanced algorithms anymore. You just have to teach a computer to come up with its own advanced algorithm.

  • What is Machine Learning?

    Machine Learning more accurate in predicting outcomes without being explicitly programmed. Machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output.

  • How it works?

    To get the most value from machine learning, you have to know how to pair the best algorithms with the right tools and processes. Ophel Computing combines rich, sophisticated heritage in statistics and data mining with new architectural advances to ensure your models run as fast as possible – even in huge enterprise environments.

  • Machine Learning Algorithms We used so far

    Ophel Computing Belives You don’t need to come up with advanced algorithms anymore. You just have to teach a computer to come up with its own advanced algorithm.

  • Where it will be useful?

    Facilitates Accurate Medical Predictions and Diagnoses, Increases the Efficiency of Predictive Maintenance in the Manufacturing Industry, Easy Spam Detection, Simplifies Time-Intensive Documentation in Data Entry and Improves Precision of Financial Rules and Models.

Traditional Approach
Machine Learning
1. Traditional business applications have always been very complicated and expensive. 1.Machine Learning which focused on predictive analytics. Input we give and output we get. algorithms which plays an vital role in machine learning.
2. The amount and variety of hardware and software required to run them are daunting. 2.Machine Learning which could be helpful in reducing human errors.
3. You need a whole team of experts to install, configure, test, run, secure, and update them. 3. Easy to configure, man power will b reduced.
4. When you multiply this effort across dozens or hundreds of apps, it’s easy to see why the biggest companies with the best IT departments aren’t getting the apps they need. 4. Complicated results can be sorted through machine learning.
5. Small and midsize businesses don’t stand a chance. 5. Machine Learning can be used in small scale industries and also it can be used in large scale industries.

Machine Learning

Machine Learning Platforms

  • H2O
  • Apache PredictionIO
  • IBM's Watson
  • Eclipse Deeplearning4j
  • Machine Learning Tools

  • AI-one
  • Deeplearning4j
  • Apache Mahout
  • Open Neural Networks Library
  • sectors which are benefited by Machine Learning

  • Manufacturing
  • Retail
  • Telecommunication Services
  • finance

  • ophel computing Believes A machine can do wonders, make it learn and it can do miracles.

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