βEmpowering innovation through AI and MLβ
The intersection of AI and ML is an area that I am extremely passionate about, and I am constantly amazed by the breakthroughs and innovations that are emerging in this space.
From natural language processing and computer vision to deep learning and reinforcement learning, I am fascinated by the possibilities of these technologies and their potential to drive business value, improve customer experiences, and enhance our daily lives.
I am excited to be a part of this vibrant community, collaborating with others to explore the frontiers of what is possible and pushing the boundaries of AI and ML.
Expertise
Computer Vision ββββββββββ βοΈ 70%
Tabular ββββββββββ βοΈ 89%
Deep Learning ββββββββββ βοΈ 100%
Natural Language Processing βββββββββ βοΈ 10% γ Translation ββββββββββ Text2Text generation ββββββββ γ
Audio βββββββββ βοΈ 30% γ Automatic Speech Recognition ββββββββββ Speech2Text generation βββββββββ γ
π My Stacks
Programming Languages
Techs, Frameworks & Tools
Hosting & Data Storage
π₯οΈ Projects</>
| Name | About |
|---|---|
GamerDNA!  |
A machine learning-based application designed to analyze online gaming behavior and predict player engagement levels. Select relevants features to identify different playerβs gaming behavior. |
STT Translate & Transcribe!  |
Gradio_GenerativeAI Tool To Transcribe & Translate Audio. Enabling users to convert spoken words into written text. |
| Winged Wonders | A Deep Learning Approach to Butterfly Species Identification π¦πΏ (with Tensorflow framework, Data augmentation technique and MobileNetV3Large model pre-trained on ImageNet) |
| Potato Disease Detection | A Machine Learning Tool to Prevent the Spread of Potato Diseases π₯. Generating prediction based on 4 pre-trained models: RESNET, Inception, Xception & NASnetMobileVenn. Model performance is enhanced with Hyperparameter Tuning. |
| Heart Disease Predictor | A project aims to develop a machine learning model capable of predicting heart disease using a comprehensive dataset of key indicators π«. EDA π. A range of machine learning models, including Decision Trees, Random Forests, Gradient Boosting,β¦up to 13 comparisons and more. Hyperparameter Tuning using GridSearchCV and RandomizedSearchCV for highest test accuracy. |
| Clustering Connoisseurs | A Machine Learning system which accurately classify wines π· based on their chemical profiles, and provide insights into the characteristics of different wine styles.. Using a combination of exploratory data analysis EDA π, data preprocessing, feature scaling π§, and K-Means π to clustering, and πvisualization to identify patterns and relationships in the wine data. |
| Employee Management Program | A Python π software To Manage Employee Profile & Salary. With functions to Edit [Departments & Employee] profiles and Display net salaries. Combination of personal tax, late-coming days calculating and data parsing, re-serializing codes. |