Machine Learning

Though often confused with AI, machine learning (ML) is where we currently stand in our quest to achieve actual (or sentient) artificial intelligence. After all, while at the moment, we are not yet able to hold full-blown conversations with our devices, today’s machine learning (ML) algorithms have ushered in a brand new era in automation.

Machine Learning (ML) is an important technology that allows computers to learn from data and make predictions or decisions without being explicitly programmed. ML is becoming increasingly popular as a tool for businesses and organizations of all types to gain insights, automate processes, and improve performance. Machine learning software development is also an emerging field with many opportunities.

The most popular types of ML at the moment include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is used to predict outcomes based on labeled data, unsupervised learning is used to identify patterns or structure in unlabeled data, and reinforcement learning is used to train models to make decisions in complex, dynamic environments.

Machine Learning (ML) Development

How Does Machine Learning Work?

As the name implies, with the help of some very clever algorithms, machine learning allows us to feed data into our machines (known as “training data”) to automatically perform predictions without any explicit programming — as if they’d learnt to do it all by themselves. In this way, we can develop software solutions to scan images for objects and faces, respond to specific voice commands, and recognise other useful patterns.

Supervised Learning

In supervised learning, algorithms make use of both training data and human feedback to understand the relationship between a given set of inputs and outputs. Popular use cases include forecasting sales, generating personalised recommendations, predicting equipment maintenance and allocating human resources.


Clustering is an unsupervised machine learning technique that is used to make sense of unstructured data. This is done by grouping data points with similar properties and features together. Clustering may be used to identify fake news and spam, classify network traffic, bring together marketing targets and organise important documents.

Structured Prediction

Structured prediction involves a wide variety of supervised ML techniques that enable developers to predict structured objects (as opposed to scalar discrete or real values). We use structured prediction in a number of exciting fields including natural language processing, computer vision, speech recognition and bioinformatics.

Anomaly Detection

With the help of AD algorithms, we can pinpoint specific outliers with relative automated ease, monitor valuable metrics and single out suspicious activity. These algorithms are employed in fraud detection, sensor data correction, advertising campaign optimisation, seismology and health diagnostics.

Reinforcement Learning

Reinforcement learning is unique in that it may be considered a semi-supervised learning ML model. Like in a game of Pacman, the technique allows software agents to interact with a given environment in order to maximise cumulative rewards. It is seen in a number of areas including robotics, traffic light control and chemistry.

Neural Networks

The current pinnacle of machine learning technology, in artificial neural networks, we base our systems on connected nodes known as “artificial neurons,” and thus strive to mimic the human brain. ANN systems may be employed in everything from facial recognition software to forecasting market movements.

Where Machine Learning Is Used

Data Mining
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SEO – Neural Matching
Computer Vision
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Search Engine Optimisation
Email Filtering
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Pattern recognition
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Natural language processing

Is It Time For Machine Learning To Power Your Business?

Which ML Framework Should You Use?

Proprietary Machine Learning Software

IBM Watson Studio

Google Cloud Vertex AI


Azure Machine Learning


Amazon Machine Learning


Open Source Machine Learning Frameworks




Apache MXNet



Case Studies

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