The practical use of machine learning
Identify trends and patterns
Machine Learning will allow you to identify typical behaviors and discover relationships in large data sets, which would be impossible for humans to identify.
Use image recognitionMachine Learning will allow you to analyze and process the data provided in the form of photos and images. This functionality has a practical application and is already widely used, e.g. in OCR systems, road sign recognition systems or in systems assessing the wear and tear of industrial elements.
Make decisions faster
Machine Learning will allow you to perform a quick analysis and provide forecasts in real time. Algorithms will help you to make key decisions, and can even make them on their own.
Discover the potential of voice recognitionVoice recognition is often used by chatbots or virtual assistant programs, e.g. to identify users. Use voice recognition technology which, thanks to suitable algorithms, allows the application to be used for continuous learning and offers new possibilities in the field of communication with devices and people.
Outsourcing of Machine Learning engineers and teams
JCommerce provides the necessary competences and implements Machine Learning projects, including projects in the field of neural network design, deep learning and data science.
Development services for Machine Learning solutions
JCommerce specializes in the development of Machine Learning technologies for clients from the banking, financial, e-commerce and healthcare sectors. The solutions created by our specialists include e.g. a system for analyzing document scans, a customer support system with a chatbot application and a system for the automatic analysis of financial data.
See technologies used in Machine Learning projects
We offer comprehensive support in ML projects at the following stages:
- collecting requirements
- designing solutions
- prototyping
- development
Machine Learning in FinTech | Document scanning classification system
One of the leaders in the European financial sector was looking for a solution that would support and improve its document flow system. Until now, each document sent to the bank was scanned and sent to the right employee by the administration department. However, this process took up too much time.
The goal of the project was to create a system that could perform document analysis for employees, then classify the document on the basis of the data read and assign it to a specific department and person in the company.
We designed a ML solution based on an algorithm for image classification and transfer learning. The algorithm for classifying images made the recognition of text from document scans possible, which was followed by analysis for characteristic parts of the text. The transfer learning algorithm ensured the proper learning process of the entire solution based on the knowledge acquired during work.
Machine Learning in banking sector | Intelligent search engine for the chatbot
A Europe-wide bank used a chatbot which featured a search mechanism based on keywords that was used to handle some of the queries. In order to get an answer to a query, the client had to enter specific phrases in the search engine. This meant that the knowledge base required significant involvement from the consultant side. To reduce the volume of this type of query, the bank decided to create a new search engine.
The underlying assumption of the project was to eliminate the simple search and replace it with more advanced mechanisms based on semantic searching.
The designed solution is based primarily on NLP (Natural Language Processing) algorithms. The new chatbot expands its knowledge base using deep learning algorithms. Thanks to this, based on the analysis of the answers given by consultants, the system generates answers to previously unknown questions.
Machine Learning in Industry 4.0 | Analysis of the wear and tear of industrial elements using image analysis
An industrial company that produces mechanical components needed a way to optimize the exchange of cutting tools on CNC machines. In the event that the head was replaced too late, the material being treated was damaged. In turn, if replaced too early, the replacement generated additional costs associated with the purchase of new tools.
The plan was to determine the best time for the service by means of a Machine Learning solution. In order to do so, a device was created to take a photo of the head after each work cycle. The algorithm used image analysis to determine the rate of wear and tear of the head in a given cycle. On this basis, it forecasted whether the given head was suitable for operation in the next cycle or whether it had to be replaced.
The use of a solution based on Machine Learning technology translated directly into a reduction of costs and the maximum use of the purchased tools.
Machine learning technologies
Programming environments
- PyCharm
- Jupyter
Programming languages
- Python
- SQL
- Bash
Machine Learning libraries
- Scikit-learn
- TensorFlow
- PyTorch
- H2O
- XGBoost
Libraries for data
- Numpy
- Pandas
- SciPy
- Dask
Big data
- Hadoop DFS
- Spark
- Hive
- Kafka
What do you need to know about Machine Learning? – FAQ
What is Machine Learning?
Machine Learning is technology that provides applications (algorithms) with the opportunity to learn. Based on the data provided, programs identify dependencies and trends, creating ready-made solutions that can be implemented to automate work or create completely new opportunities.
What is Deep Learning?
Deep Learning is a field of Machine Learning technology, the underlying assumption of which is the use of neural networks for learning.
What is transfer learning?
Transfer Learning is the adaptation of knowledge acquired during previous tasks while implementing new tasks in order to optimize this process.
What are neural networks?
These are sets of algorithms that process data in a way inspired by human neurons. Data is transmitted through millions of connections between artificial neurons contained in subsequent layers of neural networks. Each neuron connection has a parameter (the so-called weight) that allows you to store and represent complex patterns found in the data. During the back-propagation process, the parameters are modified, so that the results obtained are increasingly becoming better and more accurate.
What are NLP algorithms?
NLP (Natural Language Processing) algorithms allow applications to recognize human voices and text. They enable interaction between humans and computers.
How is Machine Learning related to Artificial Intelligence?
ML is the element of Artificial Intelligence that allows programs to learn. This process includes the analysis and interpretation of behavior, trends and patterns. On this basis, AI programs perform specific actions and are able to make predictions even based on data that has not previously been observed.
What are Machine Learning algorithms used for?
The most common examples of the use of Machine Learning algorithms are the functions of automatic data analysis and classification, voice and image recognition. The most popular examples of the application of machine learning technologies include algorithms used on portals such as Netflix, Spotify or YouTube, where a personalized list of suggestions is created based on the multimedia that the user plays. Apple’s voice assistant, Siri, is also based on ML mechanisms that teach the program to recognize the voice and the way in which the owner speaks. The Google Translator has a function which recognizes and translates text which is visible on the recorded image with the use of a smartphone camera in real time, and intelligent cars design optimal parking by analyzing images from the cameras.
Contact us!
Consult with our experienced Machine Learning engineers on your machine learning project. Describe your idea to us and we will offer you solutions that will help you bring it to life.