Parth Maniar

Hey there, I'm Parth Maniar—a data enthusiast, always on the lookout for new challenges and opportunities to make a meaningful impact. With a Masters in Data Science from the University at Buffalo, I've built a solid foundation in the world of data science and machine learning. Through coursework and hands-on projects, I've honed my skills in areas like predictive analytics, automation, data modeling and software engineering.

where I seamlessly merge clinical insights with cutting-edge technology. My role is a dynamic blend of data engineering and software development—leveraging AWS, large language models, and advanced dashboard creation—to integrate diverse data streams, from clinical records to imaging data, into actionable insights.

Every day presents a new challenge as I push the boundaries of technology in healthcare. From developing optimal data pipelines and integrating advanced LLM projects to enhancing overall analytical capabilities, my work is driven by a passion for innovation and excellence. I'm dedicated to transforming complex data into clear, actionable insights that not only elevate clinical decision-making but also set the stage for future breakthroughs.

Exploring how Retrieval Augmented Generation helps large language models access external knowledge, reduce hallucinations, and deliver more reliable responses by knowing when to retrieve rather than generate.

Retrieval Augmented Generation: Building LLMs That Know Their Limits

Explore the difference between AI Agents and Agentic AI with real-world examples.

AI Agents vs Agentic AI: The Critical Distinction Explained

Discovering the Power of Dimensionality Reduction - Unveiling patterns and visualizing data with PCA and t-SNE.

Understanding PCA and t-SNE: A Comparative Analysis for Dimensionality Reduction

Hyperparameter tuning in machine learning, including methods such as grid search, random search, and Bayesian optimization and providing code snippets to demonstrate how to use these techniques in practice.

Cracking the Code: Mastering Hyperparameter Tuning for Optimal Machine Learning Performance

Model interpretability in machine learning, including the use of LIME and SHAP techniques

Unlocking the Secrets of Machine Learning: Techniques for Interpreting Complex Models

Utilize the power of Facebook Prophet to identify anomalies in your time series data and safeguard your data from potential issues with the help of Python.

Uncovering the Unusual: Using Facebook Prophet for Anomaly Detection in Time Series Data

How to identify and remove outliers from data using Python, using techniques such as visualization, z-scores, and interquartile range (IQR).

Out with the Outliers: How to Remove Outliers in Python

Debugging a model or data science project involves following a systematic approach to identify and fix issues at each stage of the pipeline, starting with the data loaders and ending with the outputs.

Debugging a model or any data science project

Regression is a supervised learning algorithm used to predict continuous or real-valued outputs for given inputs, and is widely applied in a variety of fields to make accurate predictions.

The Ins and Outs of Regression: Types, Training, and Evaluation

Find the missing data percentage and count in pandas

Missing Data Percentage and Count in pandas

Getting the Spotify Refresh Token doesn't need to be a hassle any longer.

How to get the Spotify Refresh Token

Apple announced that it would begin checking content uploaded to iCloud Photos, against a list of known CSAM material. This is fine on it's own, however the system could easily be abused.

Apple doesn't care about privacy, and it's showing.

The EU announced plans to force all devices – including iPhones – to use a common charging port.

Hi, I am Parth Maniar

Passionately bridging the gap between customers and businesses with the transformative power of Artificial Intelligence, guided by data-driven insights.

Latest

Retrieval Augmented Generation: Building LLMs That Know Their Limits

AI Agents vs Agentic AI: The Critical Distinction Explained

Understanding PCA and t-SNE: A Comparative Analysis for Dimensionality Reduction