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armahdavi/README.md

Hello World! πŸ‘‹πŸŒ

This is Alireza. I am passionate about Data Science, Machine Learning, Deep Learning, and Large Language Model (LLM) Prompt Engineering.

Overwhelmed under a pile of mixed and complex data and endless information generated from day to day?!🀯 Worries notπŸ™‚. I can help✨.

I will navigate you safely and peacefully through the rough sea of mixed and complex data to the right destination where you can see trends, capture anomalies, detect and prevent loss, and optimize benefits 🌊 🚒.

I will help you streamline operations, optimize strategies, accelerate goal achievement, and enhance client satisfaction by uncovering actionable insights from your work, business, client, and contractor data πŸ’ΌπŸ’πŸ’°.

As a seasoned Data Scientist with close to a decade of experience in managing and optimizing diverse datasets across academic, engineering, finance, and business domains, I'll be your next friend and colleague for life, so let's collaborate πŸŽ“πŸ”¬πŸ› οΈβš™οΈπŸ“ˆπŸ’°πŸ“ŠπŸ’»πŸ“πŸ€.

  • πŸ”­ I’m currently working on LLM Prompt Engineering to train and tune LLM (with Scale AI)πŸ€–πŸ‘Ύ
  • 🌱 I’m currently learning the architecture of an LLM leading to text generation (with DeepLearning.AI)πŸ“πŸ‘·β€β™‚οΈ
  • πŸ‘― I’m looking to collaborate on work automation, budget estimation, Stock Market prediction, and business intelligenceπŸ“ˆπŸ—“οΈπŸ’°βš™οΈ
  • πŸ€” I’m looking for help with upgrading GPU multi-core parallelization for cuDF and PySparkπŸ’¦πŸŒˆ
  • πŸ’¬ Ask me about any data science, ML, DL, and Python relevant topicπŸ’»
  • πŸ“« How to reach me: ar.mahdavi21@gmail.com, alireza.mahdavi@mail.utoronto.caπŸ“§
  • πŸ˜„ Pronouns: He/Him πŸ€΅β€β™‚οΈπŸ‘¨πŸ™‹β€β™‚οΈ
  • ⚑ Fun fact: I hated and was very bad at programming until I started my Ph.D., πŸ™ˆπŸ˜‚

Skills and Experience

Programming & Scripting Languages

python sql vba stata R

Python, SQL, Visual Basics for Applications (VBA), Stata, R

Data Science, Machine Learning, & Deep Learning

pandas numpy rapid_cudf scikit_learn tensorflow

Pandas, Numpy, CuDF, Scikit-Learn, XGboost, TensorFlow, OpenCV, Polars

Data Plotting & Visualization

matplotlib seaborn seaborn pandas bokeh

Matplotlib, Seaborn, Plotly, Pandas, Bar Chart Race, Bokeh

Text Mining & Natural Language Processing (NLP)

nltk spacy

Regex, Natural Language Toolkit (nltk), SpaCy, TextBlob

Statistical Analysis

scipy statsmodel

Scipy, StatsModel

Machine Learning Operations (MLOps)

docker kubernetes flask

Docker, Kubernetes, Flask

Cloud

gcp aws databricks

GCP, AWS, DataBricks

Big Data

spark

PySpark, Spark SQL, Polars, CuDF

RDBMS

mssql

MS SQL Server

Climate Change and Envrironment

MeteoStat, PyThermalComfort

Pinned Loading

  1. code-data-analytics-pipelines-statistics-ML-PM-PSD_residential-QFF code-data-analytics-pipelines-statistics-ML-PM-PSD_residential-QFF Public

    Sharing all the data pipelines and processing codes, statistical modellings, descriptive statistics, plot visualizations, and machine learning from Mahdavi & Siegel (2021) (Indoor Air) Project Mies…

    Jupyter Notebook

  2. ML-xgboost-regressor---rapid-filter-forensics_rff-dust-recovery-from-HVAC-filter ML-xgboost-regressor---rapid-filter-forensics_rff-dust-recovery-from-HVAC-filter Public

    ML modelling of dust recovery from HVAC filters: Linear Regression vs. XGBoost - Project Milestone: 2017-2020.

    Jupyter Notebook

  3. qff-evalation_code-data-processing-statistics-plotting qff-evalation_code-data-processing-statistics-plotting Public

    Data pipelines and processing codes, statistical modellings, descriptive statistics, and plot visualizations for QFF evaluation phase of for Mahdavi et al. (2021) (Environmental Pollution) Project …

    Jupyter Notebook

  4. IBM-Statistics-Codes IBM-Statistics-Codes Public

    This Repository includes all the codes in Statistics that I have developed from my previous projects

    Jupyter Notebook

  5. unsupervised-clustering-ml---pm_source_detection---how-extensive-do-you-cook-or-smoke----AI-can-help unsupervised-clustering-ml---pm_source_detection---how-extensive-do-you-cook-or-smoke----AI-can-help Public

    Indoor PM2.5 source detection algorithm using unsupervised clustering ML method

    Python 1

  6. code-data-processing-statistics-plotting-airborne-sampling-of-PM code-data-processing-statistics-plotting-airborne-sampling-of-PM Public

    All codes for the data pipelines processing, statistical modellings, descriptive statistics and plot visualizations from airborne phase of Mahdavi et al. (2021) (Environmental Pollution) Project Mi…

    Jupyter Notebook