Hi, I'm Zainab Siddiqui, a passionate AI Developer with experience in developing models for complex problems.
- Programming: Python
- Machine Learning & Deep Learning: TensorFlow, PyTorch, Scikit-learn, Pandas, Numpy
- Generative AI & Agentic AI: LLMs, HuggingFace, Langchain, Langgraph, CrewAI, Semantic Kernel, AutoGen
- Big Data: Hadoop, Hive, HBase, Spark (PySpark), MapReduce, YARN
- Web Development: Streamlit, Gardio, Flask
- Tools: Git, Jupyter, PyCharm, Google Colab, VS Code, Knime, Alteryx, PowerBI, Tableau, DataBricks, AWS Sagemaker, FastAPI
A machine learning model for generating song lyrics using advanced neural network techniques. This model leverages Bi-directional LSTM to create coherent and creative lyrics in various styles.
A deep learning model for accurate classification of 10 fruit categories. The objective is to build a model that can accurately identify the type of fruit based on images.
A computer vision project for detecting student concentration levels using image classification techniques. The concentration levels can be broadly one of these two: engaged and not engaged, which have 3 sub-categories each. So, the model predicts one level out of 6.
A project about predicting heart disease using machine learning models and ensemble methods. It is a classification of a patient based on clinical parameters into healthy and not healthy in terms of heart. Evaluations are done using the Cleveland dataset.
A web application for sentiment analysis built using Streamlit. The application analyzes digital text to determine if the emotional tone of the message is positive, negative, or neutral.
- PGP in Data Science, Great Lakes (in association with UT Austin) (2022)
- Bachelor's of Technology (2017)
- LinkedIn: Zainab Siddiqui