Python

Hi guys I am Anand, and this is my first blog post on blogger.

I am Machine Learning Engineer. This post is to show you some basics of python. 

PYTHON

What is Python?

           Python is one of the most famous programming languages out there available as an opensource. It is famous for its very simple syntax, it'll actually feel like reading an English poem. It is so versatile that almost every domain in computer science actually uses python. take for example:

• 1. Web Development

  • Frameworks: Django, Flask, FastAPI

  • Used for: Building websites, APIs, and backend systems.

  • Example: Instagram, Pinterest, Spotify use Python in their backend.

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  🔹 2. Data Science & Machine Learning

  • Libraries: NumPy, Pandas, scikit-learn, TensorFlow, PyTorch

  • Used for: Data analysis, AI models, recommendation engines, NLP, computer vision.

  • Example: Netflix uses Python for recommendation systems.

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  🔹 3. Automation & Scripting

  • Libraries: Selenium, PyAutoGUI, Requests

  • Used for: Automating repetitive tasks, web scraping, file handling, testing workflows.

  • Example: Finance companies automate report generation with Python scripts.

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  🔹 4. Cybersecurity & Ethical Hacking

  • Libraries: Scapy, pwntools, paramiko

  • Used for: Writing penetration testing tools, malware analysis, automating security tasks.

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  🔹 5. Game Development

  • Libraries: Pygame, Panda3D, Godot (supports Python via GDScript-like API)

  • Used for: Prototyping and building small to medium games.

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  🔹 6. Finance & Fintech

  • Libraries: QuantLib, zipline, pandas, TA-Lib

  • Used for: Algorithmic trading, risk analysis, financial modeling.

  • Example: Hedge funds use Python for backtesting trading strategies.

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  🔹 7. Embedded Systems & IoT

  • Tools: MicroPython, CircuitPython, Raspberry Pi

  • Used for: Programming sensors, IoT devices, robotics.

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  🔹 8. Cloud Computing & DevOps

  • Tools: Ansible, SaltStack, Terraform (plugins in Python), AWS Boto3 SDK

  • Used for: Infrastructure automation, deployment, cloud service scripting.

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  🔹 9. Scientific & Engineering Applications

  • Libraries: SciPy, SymPy, Biopython, Astropy

  • Used for: Physics simulations, biology/genomics research, astronomy data analysis.

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  🔹 10. Desktop Application Development

• Frameworks: PyQt, Tkinter, Kivy

• Used for: Cross-platform desktop app

        In short: Python is everywhere. But the most dominant and growing domains are AI/ML, Data Science, Web Development, and Automation.

Why you need to learn python?

        Again, for reminder, Python is one of most famous and most growing programming language. It is used almost in everywhere. If you're gonna start coding the best first language is python because you can do the first code in like seconds.

        Python is so beginner friendly, it has one of the biggest ecosystems of libraries, one language multiple applications. Massive open-source community. Python consistently ranks among the top 3 most in demand programming language.

History of Python

        It's not very essential to know the history of any language before learning. But we have to give credit and respect to creators just my remembering them. 

        Python was created in 1989 by Guido van Rossum at CWI in the Netherlands, inspired by the ABC language and named after Monty Python’s Flying Circus. Its first version (0.9.0) was released in 1991, introducing core features like functions, exceptions, and data types. Python 2.0 came in 2000 with improvements like list comprehensions, but its design flaws led to a complete overhaul with Python 3.0 in 2008, which improved Unicode support and syntax consistency (though it wasn’t backward compatible).

         

        After the official end of Python 2 in 2020, Python 3 became the universal standard, and today Python is one of the world’s most popular languages, powering fields like AI, machine learning, web development, automation, and data science.

How important is Python in Machine Learning and Data Science?

         Python is extremely important in machine learning and data science — it’s basically the default language for both fields today. In Industrial Standards, almost all modern ML and data science research, tutorials, and projects use Python

    

    🔹 Huge Ecosystem of Libraries

• Data handling → NumPy, Pandas

• Visualization → Matplotlib, Seaborn, Plotly

• Machine Learning → Scikit-learn, XGBoost, LightGBM

• Deep Learning → TensorFlow, PyTorch, Keras

• Data Engineering → PySpark, Dask

          Python's simple syntax makes it easy to test ideas Quickly. Massive opensource community with tons of pre-trained models, datasets, and tutorials. Python is one of the top required skills in ML/data science jobs.

           In short, Python is the backbone of machine learning and data science. while you can use other languages like R, Julia, or C++, Python dominates because of its easy, powerful, and supported by the biggest ecosystem.

Best Resource to learn Python

        There are tons of Python tutorials out there in online. Theres YouTube, lots of courses, Textbooks. I will attach some resource down here:

Websites (beginner-friendly):

• w3schools - Python Tutorial - step by step tutorials, examples, and exercises.

• real python - Python Tutorials – Real Python - High quality tutorials and projects, great for practical learning.

• GeeksforGeeks - Python Tutorial - GeeksforGeeks - Tons of examples, exercises and interview prep.

• Python's official documentations - Our Documentation | Python.org - The official tutorial, but not that beginner friendly.

Books:

• Beginner Friendly

• “Automate the Boring Stuff with Python” by Al Sweigart - Super practical, teaches Python by automating real-world tasks (files, web scraping, spreadsheets)

• “Python Crash Course” by Eric Matthes - Project-based introduction (games, web apps, data visualization).

• “Head-First Python” by Paul Barry - Visual, engaging style. Great if you prefer less theory and more interactive learning

• Intermediate Level

• “Fluent Python” by Luciano Ramalho - Focuses on writing Pythonic, efficient code. Covers advanced topics: iterators, generators, decorators, concurrency.

• “Effective Python” by Brett Slatkin - 90+ best practices and tips for writing clean, professional Python code.

• “Python Tricks: A Buffet of Awesome Python Features” by Dan Bader - Short, practical tricks to level up your Python skills.

• Data Science / Machine Learning Focus

• “Python for Data Analysis” by Wes McKinne - Written by the creator of Panda. Must read if you want to do data science.

• “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron - One of the best books for ML using Python. Project-based, covers regression, deep learning, NLP, etc.

• “Deep Learning with Python” by François Chollet - Written by the creator of Keras. Great for understanding neural networks in Python.

• Reference / Comprehensive
•  “Learning Python” by Mark Lutz - Very detailed, covers nearly everything in the language. Best as a reference, not a first read (because it’s 1,600+ pages!).

If you're starting fresh:

1. First book: Automate the Boring Stuff or Python Crash Course.
2. Next step: fluent Python + Python for Data Analysis (if you're into ML or DS).

Interactive Coding Platforms:

• Hacker Rank - Solve Python | HackerRank - Challenges and practice problems.

• Leedcodes - Leetcode All Problems with Python/Java/C++ solutions - Excellent for Python problem solving and interview prep.

• Codewars -Basic Python | Codewars - Gamified coding challenges with Python support.

• SoloLearn Python - Welcome | Sololearn: Learn to code for FREE! - App based learning with interactive lessons.

Structured Courses:

• Python for Everybody | Coursera - University of Michigan course, beginner-friendly.

• HarvardX: CS50's Introduction to Programming with Python | edX - courses from Harvard, MIT.

• Python Bootcamps: Learn Python Programming and Code Training | Udemy - Paid but affordable, with top rated courses.

• DataCamp - Specializes in Python.

Practices and projects:

• Project Euler - Math-heavy coding challenges(great for sharpening python skills)

• Kaggle: Your Machine Learning and Data Science Community - Free Python and data science courses + real datasets to practice.

If you're just starting, I'd recommend:

• W3Schools + HackerRank (for basics & practices)

• Kaggle + DataCamp (if you want to go into ML/data science)