This repository contains Jupyter notebooks with examples of usage for common Python data science and machine learning packages. Each notebook provides insights and code snippets for working with various packages. These notebooks were created through my formal education, my informal learning online, and work that I've done in projects on my own time.
- AWS: AWS SDK for TextExtract.
- MySQL: intro to MySQL from programming with Mosh.
- Data Structures & Algorithms: intro notebooks to common leet code problems and some algorithms theory.
- NumPy: numpy basics and tutorials.
- Pandas: pandas basics and tutorials.
- PySpark: pyspark basics and tutorials.
- PyTorch: pytorch introduction to tensors and ANN/CNN.
- Regression Algorithms: solving common regression problems and how to instantiate a regression class.
- Scrapers: scrapy, requrests, beautiful soup, and API's.
- TensorFlow: introduction to tensors.
- Clone this repository to your local machine.
- Open the desired Jupyter notebook using editor of choice or colab
- Follow the instructions and code examples provided in each notebook to learn and practice.
Feel free to explore, experiment, and contribute to this repository by adding more notebooks or improving existing ones.