ummadiviany / master-guide

Master guide to become an expert in broad areas of computational intelligence

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Master Guide for Experts

Portfolio

  1. Webpage (preferably hosted on github.io)
  2. High quality resume (typed in latex)
  3. LinkedIn profile (take skill tests, get recommendations)

Tools/Extensions

  1. GitHub (command line)
  2. Linux commands (top 25)
  3. Bash scripting (basics)
  4. VSCode
  5. Working with remote computers/Docker via SSH (VSCode is best for this)
  6. Virtual envs (conda, pipenv, venv, virtualenv)

Python Libraries

  1. Web scraping & automation

    1. requests, requests_html, httpx (For HTTP requests. Learn to Develop & hack APIs)
    2. BeautifulSoup (for parsing html/text)
    3. Selelnium (most powerful automation tool, must learn. People use it to build twitter bots to testing products at google/meta)
    4. scrapy (popular scraping framework)
  2. Testing

    1. unittest, doctest, pytest (testing units of code)
    2. locust (Scalable user load testing tool)
  3. API Devlopment

    1. FastAPI (FastAPI framework, high performance, easy to learn, fast to code, ready for production)
    2. Flask & flask-restful (micro framework for building web applications)
    3. django, django-rest-framework(python backend and API devlopment), celery (Distributed Task Queue)
    4. API Testing
      1. httpie (test APIs in terminal / Web)
      2. Postman
  4. Visualization

    1. bokeh (Interactive Data Visualization in the browser)
    2. Plotly (Interactive graphing library for Python)
    3. matplotlib (Plotting for Python)
    4. altair (Declarative statistical visualization library for Python)
    5. Redash (Connect to any data source, easily visualize, dashboard and share your data)
  5. Data

    1. pandas (powerful data analysis / manipulation, data.frame objects, statistical functions, and much more)
  6. Matrix

    1. numpy (Fundamental package for scientific computing)
  7. Deployments

    1. Streamlit (The fastest way to build data apps in Python)
  8. Machine Learning & Deep Learning

    1. sckikit-learn (machine learning in Python)
    2. Keras ( Deep Learning for humans)
    3. PyTorch (Tensors and Dynamic neural networks in Python with strong GPU acceleration)
    4. Tensorflow (Open Source Machine Learning Framework)
  9. Others simple yet powerful (must use)

    1. attrs (Python Classes Without Boilerplate)
    2. asyncio
    3. re — Regular expression operations
    4. math — Mathematical functions
    5. random — Generate pseudo-random numbers
    6. statistics — Mathematical statistics functions
    7. itertools — Functions creating iterators for efficient looping
    8. glob — Unix style pathname pattern expansion
    9. shutil — High-level file operations
    10. pathlib — Object-oriented filesystem paths
    11. os — Miscellaneous operating system interfaces
    12. io — Core tools for working with streams
    13. time — Time access and conversions
    14. argparse — Parser for command-line options, arguments and sub-commands
    15. logging — Logging facility for Python
    16. threading — Thread-based parallelism
    17. multiprocessing — Process-based parallelism
    18. concurrent.futures — Launching parallel tasks
    19. json — JSON encoder and decoder
    20. typing — Support for type hints

Languages

  1. Python
  2. SQL(PostgreSQL)

Additional

  1. Writing good markdown readme for projects/repos

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Master guide to become an expert in broad areas of computational intelligence