Machine learning
development services

Statistics show that the healthcare sector can save as much as 70% of drug discovery costs with the applications of ML in healthcare.

The areas with the most significant machine learning use potential in healthcare are ML-based diagnosis, early pandemics identification and imaging diagnostics.

Use cases:

• Disease identification and diagnosis
• Treatment personalization
• Remote monitoring and wearable devices

With the low data SNR and large volumes of legacy data, machine learning is a right tool for the financial ecosystem.

Almost 70% of financial services companies already use ML, forcing other financial institutions to rethink their traditional approaches to handling financial activities on the market.

Use cases:

• Algorithmic trading
• Underwriting and credit scoring
• Financial monitoring

Machine learning development allows retailers to increase their sales by 20% and reduce inventory costs by 30%.

Amazon’s recommendation engine, powered by machine learning, accounts for at least 35% of its total sales by suggesting products based on customer behavior.

Use cases:

• Demand forecasting
• Inventory management
• Personalized marketing

In the manufacturing industry there are 2 critical pillars - quality control and process optimization. With ML applications, manufacturers are set to achieve unparalleled precision and efficiency in these fields.

According to a McKinsey report, companies adopting AI and ML-driven strategies in manufacturing have witnessed a 30% to 50% reduction in machine downtime, 15% to 30% improvement in labor productivity, 10% to 30% increase in throughput, and 10% to 20% decrease in the cost of quality.

Use cases:

• Predictive maintenance
• Quality control
• Supply chain optimization

Used for predictive analysis. It models the relationship between a dependent variable (target) and one or more independent variables (features) by fitting a linear equation to the observed data.

For instance, predicting housing prices based on factors like square footage, number of bedrooms, and location involves finding the best-fit line that minimizes the difference between the actual and predicted values.

The simplicity of linear regression makes it a powerful tool for understanding relationships within data, though it assumes a linear relationship that might not always be present.

Used for binary classification problems, where the outcome is a categorical variable with two possible values, such as spam vs. not spam or disease vs. no disease. Uses a logistic function to model the probability of the default class and outputs values between 0 and 1.

For example, logistic regression can predict whether a customer will buy a product based on their browsing behavior and purchase history. The sigmoid function applied in logistic regression helps in mapping predicted values to probabilities, making it suitable for binary outcome predictions.

Unsupervised learning technique that groups data points into clusters based on their similarities. Useful for identifying hidden patterns and segmenting data for further analysis. Widely used in market segmentation, image compression, and anomaly detection.

Another popular method, hierarchical clustering, builds nested clusters by progressively merging or splitting them based on a distance metric.

A versatile Machine learning algorithm capable of performing both classification and regression tasks. They split the data into subsets based on the value of input features, creating a tree-like structure where each node represents a feature, each branch a decision rule, and each leaf a target outcome.

Can be used to classify emails as spam or not spam by evaluating features like the presence of certain keywords. Decision trees are intuitive and easy to interpret, but they can become complex and prone to overfitting, especially with noisy data.

Enhance the predictive power and robustness of decision trees: construct multiple decision trees during training and merge their results. Each tree in the forest is trained on a random subset of the data and features, which helps in reducing overfitting and improving generalization.

In predicting loan defaults, a random forest would aggregate predictions from numerous decision trees, resulting in a more accurate and stable prediction model.