Hi there, I'm Rishi 👋🏼👨🏻‍💻

I am into a Machine Learning, Deep Learning, Computer vision and NLP with a strong foundation in Statistics, Mathematics, and Computer Science. I have extensive experience in developing and deploying Machine Learning models for various domains, including healthcare, finance, and e-commerce. Some of my notable skills and achievements include:

Successfully deployed and maintained Machine Learning models using AWS services like Sagemaker, EC2, Lambda, and API Gateway.
Expertise in Deep Learning frameworks such as Tensorflow and Keras for developing complex Neural Networks and implementing Transfer Learning.
Proficient in data preprocessing techniques such as Feature Engineering, Normalization, and Handling Imbalanced Data.
Implemented and fine-tuned various supervised and unsupervised learning algorithms like Random Forest, SVM, K-Means, and DBSCAN.
Developed and deployed Computer Vision models for Object Detection, Image Segmentation, and Classification using frameworks such as OpenCV, PyTorch, and Tensorflow.
Strong understanding of Natural Language Processing (NLP) techniques such as Text Classification, Sentiment Analysis, and Topic Modeling.
Presented research papers at top-tier international conferences on topics such as Deep Learning and Computer Vision.
Completed several online certification courses on Machine Learning, including the Machine Learning Engineer Nanodegree from Udacity.
Active member of the Machine Learning community and regularly participate in hackathons and meetups.

I am always eager to take on new challenges and am passionate about using my skills to solve real-world problems using Machine Learning. I love to learn and contribute in any and every possible way.

⚡ Check out my portfolio site rushizirpe.github.io for all my projects and and my works.

Some of my Github Public Stats 💻

Rishi's Projects

build_etl_pipeline

This project provides a simple example of building an ETL (Extract, Transform, Load) pipeline using Python and SQL. The pipeline extracts data from a CSV file, applies some transformations to the data, and loads the transformed data into a SQLite database.

classification-with-nlp-xgboost-and-pipelines

This is a simple Flask API that provides wine variety classification based on their descriptions. The API uses an XGBoost model and an NLTK tokenizer to perform the classification.

eda

This EDA repository covers essential topics such as data cleaning, handling missing values, feature engineering, and visualizations.

estimators-from-scratch

This project provides Python implementations of various estimators from scratch, without relying on external libraries such as scikit-learn. The purpose of this project is to understand how these models work under the hood.

i-write-as-i-learn

A collection of public articles written by me on Medium. The articles cover a variety of topics including Machine Leanring, programming, data science, and more.

rag-with-llms

This repository implements a Retrieval-Augmented Generation (RAG) approach leveraging advanced Language Models (LLMs). RAG combines the power of pre-trained LLMs with efficient information retrieval, enabling context-aware and coherent content generation.

rating_prediction

The App Rating Prediction is a machine learning project aimed at predicting the rating of mobile applications. This repository contains the code and resources needed to train and deploy a model that can estimate the rating of an app based on various features and attributes.

text-generator-lstm

Automatic text generation using LSTM networks for generating text similar to a given input

transformer-from-zero

A Simple implementation of the Transformer model from scratch

travel-bot

a chatbot that assists users in planning their trips, suggesting destinations, providing information about flights and accommodations, and offering recommendations for activities and attractions. It can learn from user feedback and adapt its recommendations based on individual preferences.

vectordb

This repository contains an implementation of a Vector Database, a specialized database designed for efficient storage and retrieval of high-dimensional vectors. Ideal for applications such as machine learning, similarity search, and data analysis.

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