How EQA implements
Machine Learning?

Machine learning is a process of building computer algorithms that improve automatically through experience. Machine Learning is the way artificial intelligence systems work.

How does Machine Learning build knowledge databases to help companies achieve success?

Construct and prepare data

To build the artificial intelligence (AI) system we need to start by defining representative data. This will allow us to define the most important data for the machine learning algorithms.

The second step is to collect the desired data that will help the users achieve the desired objectives. For example, your data will be used for all employees ranging from clerical workers to executive staff, not just a specific group of employees.

Define the problem

The first step is to figure out the way that users will experience the systemFind out how ML can help in our goals and improve users’ experience. It is essential for assessing the true impact of its recommendations, predictions and decisions.The input in the development process should be from a diverse set of users.

That’s why EQA-video uses a conversational training experience. It helps to collect the data for the ML analysis and build the knowledge. This knowledge can be used by all the users to improve their everyday operations.

Build and train model

The process of machine learning begins with observations of data, such as examples, instruction, or direct experience, in order to look for patterns in data and make better decisions in the future, based on the examples that are provided.

The aim is allowing the computers to learn automatically without human assistance or intervention and adjust actions accordingly.

Deploy and monitor

When machine learning models are applied to real, live data they rarely operate with ideal perfection, even if everything in the overall design of a system is carefully crafted.

When an issue appears in live situations, you should consider whether it aligns with any existing societal disadvantages, and how it can be impacted by short-and long-term solutions.

Here is when the EQA-video team expertise comes to help you. We build the machine learning system tailored to your business needs.

Evaluate model

The ML system can make mistakes. It is important to understand what these errors are and how they can affect the user’s experience of the system. ML systems that are most valuable evolve with time in tandem with user’s mental models.

While machine learning systems are trained on sets of existing data, they can adapt with new inputs in ways that often can’t be predicted before they happen. Therefore, user’s research and feedback strategies should be adapted accordingly. Enough time to evaluate the ML systems’ performance is needed through quantitative measures of errors and accuracy as users and use cases increase.

Machine Learning for conversational videos

The science where computers discover patterns and relationships in data instead of manual programming is called Machine Learning (ML). Personalized and dynamic experiences are created with this powerful tool. In the modern world it is already used everywhere from Netflix recommendation to autonomous cars. A set of examples are provided and the computer learns patterns from the data. Machine learning is “programming with data”.In the modern world companies face a huge amount of data that should be processed and analysed for better user experience. ML is something that makes this process quick and automatic, producing models that can analyse complex data and deliver accurate results fast -even on a very large scale.

Why machine learning is important?

There is an iterative aspect of ML, which is very important. Models are exposed to new data and they are able to independently adapt the information. Those models learn from previous computations for producing reliable, repeatable results and decisions.Artificial intelligence (AI) is the broad science of mimicking human abilities and ML is its specific subset, which trains a machine how to learn.

Growing volumes and varieties of data are less expensive, and more powerful computational processing approaches, and more affordable data storage, which makes Machine Learning very popular today.By building ML building precise models, a company has a better chance to identify profitable opportunities or avoid unknown risks.

What is required for a good machine learning?
  • Data preparation capabilities.
  • High quality data, that represents your users.
  • More data – more accurate results.
  • Basic and advanced algorithms.
  • Iterative and automation processes.
  • Scalability.
  • Ensemble modeling.
Did you know this about machine learning?
  • In machine learning, a target is called a label.
  • A neural network - a kind of ML that is inspired by the work of the human brain. It is a computing system made up of interconnected units (like neurons),which processes information with responds to external inputs, relaying information between each unit
  • You use machine learning everyday. Well, when you use Google or YouTube search, Pinterest, Facebook the recommendations and results you see are actually products of heavy machine learning processing.
Case studies
A wide variety of companies from different Industries implement ML for solving their biggest problems. From social networks to healthcare and even ecommerce, machine learning can be integrated into your industry and other company activities.


Online platform for placement, search and short-term rental of private housing around the world.

Data science teams and engineers apply machine learning for classification of images and detection of objects at scale. For optimization of user experience the ability to correctly classify the room type for a given photo is incredibly useful.


For guests:
It facilitates photos re-layout and re-ranking based on distinct room types, so photos that people are more interested in will be surfaced first.

For hosts:
It helps hosts to automatically review listings to ensure they abide by high standards of marketplace.Accurate photo categorization is the backbone for these core functions.

Sale & Trade market.

A marketplace for consumer to consumer and business to consumer buying and selling new and second-hand goods.
A company builds ML models with deep image and nature language understanding


For sellers:
They benefit from simplified posting experience with image recognition.

For buyers:
It helps buyers to automatically discover more relevant listings through image search and recommendations.

Photo editing app.

Mobile phone photo editing app.

An app uses ML as a tool for guiding and personalizing each user’s creative process.The research they did suggested that users were overwhelmed by pre-sets number and stuck to using the few favourites they knew instead of trying new.ML helps users by suggesting pre-sets that suits their photos.


For users:
They benefit from simplified editing experience with image recognition and suggestion of new pre-sets.

Data analyze

What is AI Training Data?

Training data – a resource that engineers use for machine learning models development. It is used for training algorithms with the provision of consistent, comprehensive information about specific tasks. Training data is composed of a large number of data points and each one is formatted with labels and other metadata.

It usually consists of annotated text, audio, video or image. Through training data, an AI model learns how to perform specific tasks at a high level of accuracy.

Sentiment analysis
Training data is composed of sentences, tweets or reviews, as the input and there is a label indicating whether that piece of text is positive or negative.
Image recognition
Training data input is the image and the label suggests what is on this image.
Spam detection
The input for training data is a text message or an email, while the label provides information about whether the message is spam or not spam.

Despite an early red card, the team was three goals ahead.

Text categorization
The input for training data is sentences and the target suggests the topic of the sentence, such as medicine, finance, law, etc.