What Machine Learning features can be implemented in your software.png

01.02.2019 / 14:46

What Machine Learning features can be implemented in your software?

Machine Learning is making a great change in business today. It is predicted that nearly 1 million jobs will disappear by 2026.

Today machine learning usage becomes more common. There are four available ML models to use:

  1. Supervised learning
    It is generally human-intensive. Data here is labelled to formulate a supervised learning algorithm.
  2. Unsupervised learning
    It doesn’t establish data classifications by a human. The algorithm employed looks for correlations and patterns to cluster datasets that go with others.
  3. Semi-supervised learning
    It develops techniques to offer up suggestions/recommendations for labels to a human for datasets.
  4. Reinforcement learning
    It trains a system to perform a task where there are steps requiring evaluation, as each step creates dependencies on the next step. Then it manages the interaction of these steps.

What ML features can already be implemented in your software?

  • Prediction
    Machine Learning is an alternative for time series forecasting. It can easily deal with weather, market, price, advertising, popularity and other kinds of forecasting. This technical approach is able to find relationship between different data and structure it in prediction. Machine Learning algorithms can predict even a probability of patient’s death within 24 hours.
  • Classification
    Machine Learning identifies a category the data belongs to. It solves one of the greatest challenges called image recognition. Identifying shape, texture and color histograms ML classifies images by groups. It will be possible even to predict the gender by analyzing handwriting very soon.
  • Data Storage
    ML allows to make data storage both cheaper and more reliable with cloud-based tools. It helps to generate and store more data than ever to use it for the future analytics. ML storage approach provides capacity, density, throughput, latency, and I/O operations per second.
  • Analytics
    Analysis process often becomes laborious task for humans. ML can serve your needs in this field and provide you with financial, marketing or any other analytics. It will help you to define a real-time optimal pricing of a service or product. Besides descriptive and diagnostic analytics ML allows to create predictive analytics. It produce what might occur with resource usage, user behavior, problem areas and their impact on network efficiency.
  • Personalization
    Today ML is able to make real-time application personalization for extremely engaging customer experience. Customization is possible thanks to instant cloud access to internal/external data and its processing. It can make great sense in good relations between you and your customer.
  • Recognition
    Machine Learning can recognize patterns and human activity. This technology can accurately recognize objects and shapes from different angles, whether they are partly hidden or no. For example, ML can determine whether the chosen object type is presented on the received image or no.
  • Clustering
    Clustering provides unlabeled data grouping. By combining particular properties ML presents you useful groupings for a convenient work. Algorithm will also inform you if some unusual object is found.

Machine Learning provides a variety of effective business tools. It can free up time for employees responsible for analytics, predictions, classification and clustering. Machine Learning gives you an opportunity to focus on important business tasks thanks to the majority processes automation. If you doubt what way of using ML is effective for your company, Exposit is always happy to help with the right choice.