Machine Learning for Data Analytics Solution
Helping your systems learn from the data
Supervised Machine Learning
We will make the ML algorithm understand what the Data Analytics system needs from it. For example, if the goal is for your algorithm to spot a particular object among thousands of images, the algorithm is exposed to images with and without the object and taught to precisely pick the right ones.
Unsupervised Machine Learning
This Machine Learning model is allowed to work on its own to discover the right information required for the Data Analytics system. The model finds unknown patterns in data, discovers features important for precise categorization, and works mainly with unlabeled data, without the need for manual intervention.
Semi-supervised Machine Learning
A semi-supervised ML algorithm develops a model from incomplete training data. This model is useful for Data Analytics systems that process incomplete or partly corrupted data.
Regression and Classification ML Algorithms
Regression algorithms let Data Analytics systems produce continuous outputs using parameters, such as price or length, that have values within a certain range. Classification algorithms can produce outputs limited to a certain set of values. For example, email messages are classified as either spam or not.
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Benefit From Machine Learning Solutions
What you can achieve with Machine Learning
Use Computer Vision to extract required information from the surroundings and images. Use ML to detect faces and objects, scan biometrics, control transportation, and more.
Train a Machine Learning algorithm to detect abnormal data. Use Data Analytics to detect fraud, search for information breaches, spot security issues, and other malfunctions.
Enhance predictive analytics with Machine Learning — learn what changes to expect in the future, based on past and present data, provided to you by a smart algorithm. See how your organization, its assets, equipment, or customers will change.
Natural Language Processing
Do you have to analyze and process data with human language? Train your Machine Learning algorithm to do the job for you: analyze, understand, process, and even generate natural human language.
Utilize Machine Learning to allow your Data Analytics solution to understand your customers. Let it extract useful data from their speech, answer questions, and even create chatbots to automate customer service.
Build your own algorithm like the one that powers personalized recommendations in Netflix and Amazon. Control your user’s personal experience and deliver the most relevant content.
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How We Apply Machine Learning to Data Analytics
Easy steps to harnessing the power of ML
Analyze business needs
We help you recognize what you need from an ML solution and how to build the right algorithm for your business. Then we plan the scope of work and the development process.
A very important step in which we analyze your business data, select the most useful data, and then create a suitable dataset for an ML algorithm. Then we are ready to train your future model, enhance it, and evaluate how well the model performs.
Feature engineering is our next step. Here we create required features in a raw dataset, using our thorough understanding of your business domain.
In this step we begin to train the model, feeding it raw business data. We select the most suitable approach to come up with a model that’s a perfect fit for your business. We will evaluate the accuracy of the Machine Learning model and tweak it if required.
We put the created Data Analytics model into production in reasonable time, given your business’s infrastructure.
When all previous steps are completed, we’ll continue to keep an eye on the Machine Learning model, tracking the metrics and how accurate they are with a view to making enhancements. If needed, we will test the model’s performance and work on it once more.
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