Train an Algorithm to Predict the Future

Machine Learning Algorithms for Data Analytics

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Machine Learning for Data Analytics Solution

Helping your systems learn from the data

  1. 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.

  2. 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.

  3. 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.

  4. 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

Computer Vision

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.

Anomaly Detection

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.

Predictive Analytics

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.

Customer Analytics

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.

Recommender Systems

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.

Customer Stories

450 business goals reached through tech solutions since 2016

document recognition and digital transformation

Enabling Document Recognition and Automating Invoice Processing

The customer automated invoice recognition with a precision of 70%. By digitizing the workflow, the company enhanced document processing by 8–9 times.

Technologies Used:

  • JavaScript
  • PHP
  • MySQL
  • Selenium WebDriver
  • Brightree
  • Tesseract OCR

Developing a System That Improves Customer Service of S7 Airlines

A high-load system extracting personal passenger data and building unique profiles to improve the airline’s service. With tuned prioritization algorithms, the system now identifies the company’s clients with better precision.

Technologies Used:

  • PostgreSQL
  • Python
  • Apache Airflow
  • SQLAlchemy
  • Flask
  • Alembic
  • Celery
  • Vagrant
  • Unicorn
  • GEvent
  • RabbitMQ
  • Pytest
gesture based mobile game with ML

A Gesture-Based Mobile Game With Machine Learning

The mobile app with machine learning offers users to launch rockets by shaking his/her phone, play against friends, and compare scores. As the users play the game, the system continues to polish the gesture-recognition algorithm.

Technologies Used:

  • Unity
  • Machine Learning
  • Game Center
  • Google Play Games

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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.

Process data

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.

Engineer Features

Feature engineering is our next step. Here we create required features in a raw dataset, using our thorough understanding of your business domain.

Develop Model

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.

Deploy Model

We put the created Data Analytics model into production in reasonable time, given your business’s infrastructure.

Update Model

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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Sergei Vardomatski - Founder - HQSoftware dev company

Sergei Vardomatski