Programming & Machine Learning
Python is my primary programming language for data science, machine learning, and application development. I use it extensively for data preprocessing, model building, and experimentation with machine learning and deep learning frameworks. Its rich ecosystem of libraries enables me to efficiently develop, evaluate, and deploy data-driven solutions.
Data Visualization
Matplotlib helps me visualize data patterns, trends, and model results effectively. I use it to create clear and informative plots that support data-driven insights and decision-making.
Statistical Data Visualization
Seaborn allows me to create attractive and informative statistical visualizations with minimal code. I use it to explore data distributions, relationships, and correlations through plots such as heatmaps, box plots, and pair plots, enhancing data analysis and insight generation.
Machine Learning
PyTorch is my preferred library for developing machine learning and deep learning models. I use it for its intuitive interface and dynamic computation graph, which simplify experimentation and model development. Its flexibility and strong support for GPU acceleration make it ideal for building and training complex neural networks efficiently, while its growing ecosystem aids in implementing state-of-the-art AI solutions.
Backend Web Development
PHP allows me to develop dynamic and database-driven web applications. I use it to handle server-side logic, manage form data, and integrate databases, making it suitable for building functional web systems with structured backend workflows.
Numerical Computing
NumPy is a fundamental library in my data science workflow for numerical computing and array-based operations. I use it to perform efficient mathematical computations, handle multidimensional arrays, and support data preprocessing tasks required for machine learning and scientific analysis.
Data Analysis & Manipulation
Pandas enables me to clean, manipulate, and analyze structured datasets using powerful DataFrame and Series objects. I use it for data preprocessing, feature engineering, and exploratory data analysis, making raw data ready for machine learning pipelines.
Machine Learning
scikit-learn is my primary library for implementing classical machine learning algorithms. I use it for model training, evaluation, and comparison, applying techniques such as regression, classification, clustering, and performance metrics.
Computer Vision
OpenCV is my primary tool for image and video processing tasks. I use it for preprocessing, feature extraction, and computer vision applications that support deep learning pipelines.
Database Management
I work with MySQL and SQLite to design, manage, and query relational databases. They allow me to store, retrieve, and organize application data efficiently.
ML Application Deployment
Streamlit enables me to quickly transform machine learning models into interactive web applications. I use it to create dashboards and demos that make model outputs easily accessible and understandable.
System-Level
C has strengthened my understanding of low-level programming concepts such as memory management, pointers, and structured program design. I use C to build efficient programs and to develop a solid foundation in system-level problem solving and algorithm implementation.
Object-Oriented Programming
C++ is my go-to language for learning object-oriented programming and implementing data structures and algorithms. I use it to write efficient, optimized code while applying core concepts such as classes, inheritance, and polymorphism.
Problem Solving
Data Structures and Algorithms form the backbone of my problem-solving approach. I apply them to write optimized code, improve performance, and develop scalable solutions.