Optratec Technologies | Machine Learning

Optratec Technologies is a vertically integrated IT company that like any spirited business entity runs on Ideas, emotions, grit and enterprise.

info@optratec.in
+91 90282 41325

Machine Learning Company

Being an one of the top machine learning company in India, we provide advanced machine learning solutions at 60% less cost. We have India's best machine learning and artificial intelligence development team that helps businesses think, predict & act smartly in this digital era.

AI and machine learning are taking industries by storm, arming them with advanced predictive and state-of-the-art decision-making capabilities. Machine learning SDKs, frameworks, tools, and APIs are available today that make it easier to adopt these next-gen technologies. Yet a yawning gap in skill sets is frustrating the process for many, leading to wastage of resources. Our machine learning expertise and experience fill that shortage, accelerating the business value of investments in innovative technologies.

Deep Learning

Recognition of speech, sounds, and images comes naturally to the human brain. Machines can replicate the neural system of the brain with the aid of massive computational power and advanced algorithms. Multi-layered artificial neural networks can learn patterns from millions of images and sound samples they are trained on. Deep learning enables machines to understand spoken words in real time, recognize and describe images, play games, and even diagnose diseases. In some tasks, they even outperform humans. We work on deep learning frameworks to build business applications that can supplement human faculties.

Machine Learning Expertise .

Our expertise spans the entire machine learning ecosystem

Frameworks/Libraries	Data Mining Tools	Cloud Platforms	Datasets
TensorFlow	R	Azure Machine Learning	ImageNet
PyTorch	WEKA	AWS Machine Learning	Common Crawl
Keras	RapidMiner	AWS SageMaker	PASCAL VOC
Scikit-learn		Google Cloud AI	COCO
Tesseract			CIFAR
OpenCV			MNIST
Dlib

Machine Learning Applications

Product
Recommendation

Content-based and collaborative filtering algorithms can be used to generate user-specific recommendations. These recommendations may include a set of similar items based on the common features of products chosen by users as well as items preferred by similar users.

Sentiment
Analysis

Gauging sentiments of people from voters to customers has become vital for campaigns in fields as diverse as politics and retail. Deploying natural language processing, sentiments can be mined to help build more responsive campaigns and modify brand positioning.

Targeted
Marketing

Customer segmentation using clustering can reveal the different categories that make up the customer base of a business, including the high-value ones. Each segment can be targeted with the right campaigns and products to improve customer acquisition and retention.

Energy Demand
Forecasting

Machine learning forecasting systems can predict future energy demand using past energy consumption data and weather parameters. Hybrid prediction models combining SARIMA models and machine learning techniques are also evolving. Power companies can optimize schedules and thus reduce costs and energy wastage.

Predictive
Maintenance

Continuous monitoring of machines in geographically dispersed locations is no longer difficult. Detection algorithms can identify machine deterioration by analyzing real-time machine parameters against historical data. Operators can initiate predictive maintenance, preventing irreversible damage to assets.

Automated Document
Processing

Applications that combine Optical Character Recognition, Intelligent Character Recognition, and RPA can speed up document-driven processes such as invoice processing, (supply chain management); claims handling, mortgage processing (insurance); and customer onboarding, loan processing (financial services).

Fraud
Detection

Models built on known cases of legitimate and fraudulent transactions can assign suspicion scores for new transactions and identify credit card fraud. A host of algorithms including decision trees, neural networks, regression, SVM, and k-means clustering are used for this. Predictive modeling is also used for insurance fraud detection.

Insurance
Underwriting

Predictive models based on diverse datasets (demographic and other traditional insurance data plus medical and social media data) can help underwriters determine individual risk more accurately and calculate optimum pricing. Predictive analytics can also reveal the right candidates for cross-selling.

Customer
Engagement

Voice assistants and integrated NLP-powered cognitive chatbots can accurately decode human language (both voice and text) and interact intelligently and in real time with customers. Today's deep learning-based text-to-speech systems can also provide natural human-like voices.

Health
Informatics

Knowledge created by medical research is more than what practitioners can cope up with. An intelligent system that incorporates NLP with semantic knowledge processing and machine learning can help practitioners look up research literature on specific problems.

Annotation of
Medical Records

Although electronic health records are a rich source of patient data, they do not lend themselves to analysis as they are highly unstructured. Using NLP, entities such as symptoms, diseases, and treatments can be parsed and tagged making them easily retrievable at the time of clinical decision-making.

Medical Image
Analysis

Supervised machine learning techniques are deployed in medical image analysis for computer-assisted diagnosis of certain brain disorders. Models trained on large datasets of labeled images (such as CT and MRI scans) can automatically detect indicators of disease and help doctors make a prognosis.