About MACHINE LEARNING

Machine Learning Solutions at itCraft

Machine learning might sound mysterious, but it’s present in more apps than you may think and on many devices we use. This trend looks like it will only continue to grow as more software and hardware takes advantage of the capabilities of different machine learning algorithms to provide better services. As an AI consulting company, our team can help you learn how to adopt machine learning into your project.

Machine learning is a science based on artificial intelligence algorithms. Let us explain how it works and what we can offer you as a data science consulting firm.

What is Machine Learning?

Machine learning is programming with data. That’s the simplest definition to describe it. While you can find many formal or detailed definitions of machine learning, usually, they make things more complicated than you need if you have never worked with this concept. 

A classic approach to programming is implementing algorithms – closed sets of rules written down as code in a programming language. They cover all possible paths & rules for processing the data. The developer must predict all possibilities and be precise in implementing these rules. 

Machine learning is different. The process of programming could be summed up as training a model with examples. The machine learns itself by “looking” at data samples, just like our brain learns from our experiences. So we don’t say exactly how to process the data, we only say what the right answer is, and simply let the model find the patterns inside the data by itself.

So, it’s still similar to regular programming, but the approach changes. We also need to remember that machine learning is not some magic technique that works within minutes. There are many complicated processes, but if we perform them correctly, the results can be amazing.

Learning Models in Machine Learning

Various machine learning algorithms operate using different rules. Each of these types is based on a particular learning model. Their names are: supervised, unsupervised, semi-supervised and reinforced learning. What are the differences between them?

The supervised learning model assumes that data scientists provide information with a predefined feature that the machine should “keep in mind” when looking for patterns. On the other hand, the unsupervised model doesn’t use that feature. It determines focal points on its own. The semi-supervised learning model uses methodologies from both and is perfect for significant amounts of data because it can analyze some information using previously provided labels and the rest without any labels at all. Reinforced learning needs supervision only at the beginning, to teach it how it should work with rewards.

This might seem complicated, but don’t worry. As your artificial intelligence consulting company we will provide the best solution for your product. Our team will recommend something suitable for your business requirements.

Machine Learning Example

There are many instances where machine learning can be used. Let us showcase a couple of them for you.

Machine Learning Example

Computer Vision

It’s a system that uses external hardware (like scanners, for example) to process images and turn them into digital descriptions that are ready for further processing. It can recognize objects on a picture or in a video and then help with decision making by providing certain conclusions.

Computer vision can be used for many tasks usually performed by humans, like 3D modeling, video tracking, or detecting certain events or elements. Optical character recognition (OCR) and optical mark recognition (OMR) are also computer vision examples.

Speech Recognition

It is one of the most often used AI algorithms. It uses natural language processing to recognize spoken words and “translate” them into text that can be understood by a computer.

This process is used by companies that incorporate voice bots into their operations. Other speech-related machine learning solutions include voice searches and voice UIs.

Medicine and Finances

Medical and financial companies often hire AI consulting firms because machine learning is great for conclusions and predictions. It can be used in the fight to prevent cancer. First, it learns what sick cells look like and then compares them to new images to determine if the patient might have them.

The same goes for investing – looking for patterns in particular funds can help decide which one is less risky. There are countless possibilities people can take advantage of to make their lives better.

Machine Learning Example

Computer Vision
Speech Recognition
Medicine and Finances

Computer Vision

It’s a system that uses external hardware (like scanners, for example) to process images and turn them into digital descriptions that are ready for further processing. It can recognize objects on a picture or in a video and then help with decision making by providing certain conclusions.

Computer vision can be used for many tasks usually performed by humans, like 3D modeling, video tracking, or detecting certain events or elements. Optical character recognition (OCR) and optical mark recognition (OMR) are also computer vision examples.

Speech Recognition

It is one of the most often used AI algorithms. It uses natural language processing to recognize spoken words and “translate” them into text that can be understood by a computer.

This process is used by companies that incorporate voice bots into their operations. Other speech-related machine learning solutions include voice searches and voice UIs.

Medicine and Finances

Medical and financial companies often hire AI consulting firms because machine learning is great for conclusions and predictions. It can be used in the fight to prevent cancer. First, it learns what sick cells look like and then compares them to new images to determine if the patient might have them.

The same goes for investing – looking for patterns in particular funds can help decide which one is less risky. There are countless possibilities people can take advantage of to make their lives better.

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Machine Learning vs. Deep Learning

There’s a lot of confusion between these two terms – machine learning and deep learning. Just like AI and machine learning, deep learning is a subset of ML. It uses neural networks which are inspired by a human brain. This allows it to learn new things based on examples of data provided beforehand.

Although deep learning neural networks can’t evolve like those in our brains, they are built similarly. Each layer has different functionality that is responsible for understanding one part of the information. Together, they give the computer a full piece of data that it can process and learn to detect particular phenomena.

AI consulting companies use machine learning algorithms, including deep learning, to improve software products and create solutions that can change human lives.

AI Consulting Services

As an AI consulting firm, our team can help you implement machine learning to solve your business problems. Our data science consultants are available to discuss how AI and machine learning can change your products and services.

We’ll face a variety of obstacles we might need to solve. In our approach, the machine learning consulting team will usually go with one of the two paths from the problem classification:

You can say that some of the machine learning problems are “universal”. Basically, it means that a single solution could fix various business problems. For instance, face detection could be used in:

  • Social media app that requires a profile picture with a face.
  • Detecting faces & emotions, targeting ads.
  • Taking photos at the entrance of a shopping center, counting people, helping maintain social distancing/COVID restrictions.
  • And more.

While such a problem (face detection) may not be easy to solve if we start from scratch (it might require tons of manually labeled examples and months of work), it already has some well tested, reliable solutions. Such models can easily be integrated into the app. Its complexity can be estimated with high accuracy because we hire a team of experts in API integrations and machine learning consultants.

The same goes for other popular machine learning solutions like image labeling, detecting objects, content moderation, or natural language processing.

What’s the solution?

After data science consultancy, we analyze what products (services, APIs, models) we can choose and how to use them to solve a particular problem. Then, we will recommend a machine learning solution.

Example of a generic machine learning problem

An app we developed for one of our clients was multi-language software that allows users to share their favorite book quotes on social media. The main issue we needed to solve for Postepic was to propose a solution that would enable it to take pictures of written or printed text and then transform it into an image.

We’ve decided that for this project we will use classic computer vision and optical character recognition that is available in many ready-made tools. According to our artificial intelligence consultant, there was no need to develop it from scratch, so we recommended Google Vision API. Its benefits include high accuracy, low cost and fast performance, which allowed us to achieve excellent results.

Not all business problems can be solved with the use of existing services. It depends on the case. If our data scientist consultant claims that it’s impossible to develop the project using available solutions, we’ll recommend doing an R & D project.

Machine learning consulting begins with understanding the problem well and then we start the first phase, which is research. We’ll dig into peer-reviewed articles (but not only) that describe the current state of the art in the field and our data scientists will prepare a report. It will summarize how people are trying to develop similar solutions and what we recommend.

What’s the solution?

The recommendations may include a proposal for solution architecture, ideas for experiments that can be conducted as a first step and what data is needed to start the project. Suppose our research concludes that the complexity of the problem may result in a risk of exceeding the budget.

In that case, the report can include a proposition of altering some of the features or prioritizing them in such a way that it’s possible to build an MVP solution in the first iteration of the project.

Example of a custom machine learning problem

Here’s an example of a mixed solution – a chatbot. Developing it, we can take advantage of existing services and still customize it in the training process.

A chatbot is a program that interacts with users simulating another human being. It consists of several elements:

  • natural language understanding (natural language processing) – extracting information from natural language, 
  • extracting entities, 
  • extracting intents,
  • voice recognition and text-to-speech

One of the chatbots we have implemented for a client was very business specific, so no out-of-the-box model could guarantee to “understand” the questions the users would ask using the natural language. In this case, the chatbot had to understand the concepts associated with the context.

The result of our research included the available frameworks and services with our recommendations, the summary of the most recent benchmarks and reviews that concluded which of the available technologies perform best and our suggested the architecture of an MVP.

The solution’s core was a model developed using the Dialogflow platform, where we carried out experiments based on the training data of natural language questions that may be asked. It was a middle-man between the user interface (mobile and web applications) and the backend server being the source of information brought to the end-user of a system.

Machine learning applications

Machine learning consulting companies not only recommend suitable solutions for your business. We offer much more – mainly software development with the use of algorithms. Our data scientists do the research and suggest what should be built to achieve the best results. Then, our team of designers, programmers and testers prepare a machine learning application.

Here are the steps in machine learning app development to give you a better idea of what you can expect when you collaborate with itCraft:

  • UX/UI workshops – two days of meetings where we can perform machine learning consulting, pick a technology for the project and determine all features that need to be developed.
  • Prototyping and design – it is crucial to prepare some mockups before development starts. This way, we can eliminate elements that are not necessary and focus on the core functionalities. Then, our team creates an intuitive, user-friendly interface to provide the best experience for potential customers.
  • Development – depending on the problem, the programming team might need more time to build a suitable machine learning system. Even if they use premade solutions, it still has to be adjusted to the product. Then, they need to work on other elements of the application.
  • Testing – thorough quality assurance has to be provided at all stages of production. Especially for machine learning apps, we need to make sure everything works according to requirements.
  • Release – the app is done and it’s time to show it to the world!

Machine learning technology services

There are many machine learning services we offer as an IT company. Our goal is to give you the technological advancement your business might need to expand.

  • Machine learning consulting – we always listen to our customers and propose the best solutions to meet your commercial goals. If we are not sure about custom ideas, we check out the current state of the art through research and then our team implements it.
  • Machine learning development – we create web and mobile applications that take advantage of the algorithms so you can offer a unique, innovative product to your end-user.
  • Machine learning support – if you already have a software product, we can help you grow it or update it if there’s a need.

Contact us

Start your new mobile project now

Contact us and tell us what you have in mind. Even if it’s only an idea – don’t worry! We’re here to help you. Let’s talk and decide what the next step for your business can be when collaborating with itCraft.

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