How Fingent Helps
With cutting edge apps, we help you explore the possibilities of machine learning in solving key business challenges, enabling data-driven decisions, and creating innovative business models. Ensuring reduced costs, increased time saving, automated operations and enhanced productivity, we help you accelerate the end-to-end machine learning lifecycle in your organization.
Our Machine Learning Services
Smart ML Applications
Natural Language Processing
Why innovate your business with us
Consistent high-quality results with a robust agile team and a dedicated QA practice
Highly cost-effective and best-of-breed solution with no last minute surprises
Transparent project management with maximum adherence to deadlines
Our Unique Approach & Process
By initiating your project with Fingent, you get a dedicated and skilled team backing you up round-the-clock. All our processes are customer-oriented, designed to reduce the cost of business operations, address IT resourcing challenges, and offer you a competitive edge. We start with a deep analysis of your requirements and continue our relationship with post-launch support and updates.
Research and Discovery
Validating and Shaping Idea
Design and Prototyping
Testing and Quality Assurance
Maintenance and Support
Hundreds of leading businesses have derived strategic advantages from our transformative solutions.
“Off-the-shelf products in the market couldn’t accommodate our multi-party customer relations model.”
“Fingent’s custom CRM application streamlines processes for both our builders and suppliers. They even suggested ideas to maximize efficiency.”
“Fingent helped us replace our technology with a new platform solution that included ASP pages and SQL databases. “
“They’re very good at explaining things, not overwhelming us with technical buzzwords. ”
Machine learning is a subset of artificial intelligence (AI) that employs large data sets and training algorithms to give computers the ability to learn and act without being explicitly programmed. Machine learning-powered systems have the ability to automatically learn and improve from experience.
Machine learning offers several advantages in software development. It makes coding more efficient. For instance, Google reports that 500 lines of TensorFlow code has replaced 500,000 lines of code in Google Translate. Machine learning can also be used for data and infrastructure management, automated programming, and for spotting vulnerabilities in software.
Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Machine learning is broadly classified into three types:
- Supervised Learning: As the name indicates supervised learning is a process in which we train a model or machine using properly labeled data. Once the model gets trained it will start making a prediction or decision when new data is fed to it.
- Unsupervised Learning: In unsupervised learning, the model learns through observation and finds structures in the unlabeled data. An unsupervised learning model can automatically find patterns and relationships in the dataset by creating clusters in it.
- Reinforcement Learning: Reinforcement learning follows a hit and trial method in which the agent interacts with the environment and finds out what’s the best outcome. Reinforcement learning enables a software or machine to find out the best possible path it should take in a particular situation.
The 5 most commonly used machine learning algorithms are Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, and KNN.
Data science is a broad term that comprises multiple disciplines including statistics, programming, data visualization, big data, machine learning and much more, whereas, machine learning algorithms learn models from data.
Machine learning is often considered as part of a data science project. It’s a field of study that gives computers the ability to learn without being explicitly programmed.