Data Science

ai & ml

What we do?

Data Strategy and Advisory

Data Engineering & Architecture

Predictive Analysis and ML

Natural Language Processing

AI Powered Automation

Model Deployment and MLOps

Our Process

Data Understanding and Discovery

Strategy

Implementation

Continuous Improvement

Why Choose us?

Customized Solutions

Expert Team

Proven ROI

Data Storage

Understanding problem domains

Data types(Structured, Unstructured, Semi-Structured)

Feature identification and engineering

Data Preparation

Data cleaning (handling missing values, outliers)

Normalization and standardization

Data Transformation

Feature selection and dimensionality reduction

Machine Learning

Regression (linear, logistic)

Classification (SVM, Decision Trees)

Clustering (K-Means, Hierarchical Clustering)

Deep learning foundations (neural networks, CNNs, RNNs)

Transfer learning

Reinforcement learning

Model Evaluation

Train/test split and cross-validation

Performance metrics (MSE, Accuracy, ROC-AUC)

Bias-variance tradeoff

Data Visualization

Tools: Matplotlib, Seaborn, Tableau

Visualizing model results

Storytelling with data insights

AI-Specific Applications

Predictive Analysis

Architecture

Autonomous systems (Robotics and Vehicles)

Health care Analytics and Diagnostics

Sentiment Analysis

Fraud Detection

GenAI (ex: GPT)

Real time analytics of Streaming data

Tools and Frameworks

Python Libraries

ML Libraries

AI Platforms

Big Data Tools