How I'd learn Python FAST (if I could start over)

Summary

In this video, the speaker shares a personal journey of struggling with Python in 2018 and how they eventually mastered it. They outline a practical approach for learning Python from scratch in 2025, focusing on core fundamentals, using libraries, and building projects. The speaker emphasizes the importance of learning Python for AI, data analysis, and engineering, explaining the value of hands-on projects and sharing work through platforms like GitHub. The message encourages viewers to start small, stay consistent, and demonstrate their skills by showing real-world applications and results.

Takeaways

• 😀 Focus on learning the core 20% of Python to unlock 80% of its value. You don’t need to learn everything to get started.
• 😀 Use Google Colab as your first coding environment—it's free, easy to set up, and allows you to code right away.
• 😀 Learn the core fundamentals of Python first: variables, data types, loops, and functions. Don’t worry if you don’t grasp everything on the first try.
• 😀 Libraries like NumPy, Pandas, and Matplotlib are essential. Think of them as pre-written code that saves you time and effort in coding.
• 😀 Python is like a language, and libraries are like phrasebooks that help you solve problems faster without reinventing the wheel.
• 😀 Once you’ve learned the basics and libraries, start building small projects immediately. This is where you’ll start applying what you’ve learned.
• 😀 Projects help you connect theory with practice. Small projects like a BMI calculator or a to-do list app can boost your skills.
• 😀 Momentum is key. Consistency and practice will help you grow faster than cramming syntax or doing a bunch of tutorials without applying anything.
• 😀 Don’t be afraid of sharing your work. Upload your projects to GitHub and LinkedIn to demonstrate your skills and progress, even if they're not perfect.
• 😀 The goal is not to memorize code but to solve real problems. Use Python to create practical, useful tools that showcase your abilities.
• 😀 Don't just be a consumer of AI tools—become a creator. Knowing Python gives you the ability to build, not just use, AI systems.

Q & A

Why did the speaker struggle when first learning Python in 2018?

The speaker struggled because they didn't understand when and how to apply Python, despite following tutorials, the class code, and textbooks. The overwhelming nature of the material made it difficult to grasp the concepts at first.

How does the speaker compare learning Python to learning a language?

The speaker compares learning Python to learning a new language, but they stress that understanding when to use what you’ve learned is crucial. While some people liken it to learning a language, the real challenge is in applying the concepts correctly in different contexts.

Why is learning Python still valuable in 2025, despite the rise of AI tools?

Learning Python remains valuable because AI systems still require human developers to build and maintain them. Tools like Langchain and Langraph, which are essential for AI development, are written in Python, meaning knowing Python enables you to become a builder rather than just a consumer of AI technology.

What is the speaker’s recommendation for setting up a Python coding environment?

The speaker advises using Google Colab, a cloud-based platform, for getting started with Python coding. It's easy to use and eliminates the need to install complicated software like Anaconda or Jupyter Notebook. Once comfortable, you can then move on to a more advanced IDE like VS Code.

What are the core Python fundamentals the speaker recommends learning?

The speaker recommends focusing on core concepts like variables, data types, loops, and functions. These basics can be learned for free through Python's official documentation at docs.python.org/3/tutorial. They emphasize practicing these concepts in a hands-on way to solidify understanding.

What are Python libraries, and why are they important?

Python libraries are pre-written code packages that solve specific programming problems. For example, libraries like NumPy and Pandas make tasks like mathematical calculations and data manipulation much easier. Instead of coding these solutions from scratch, you can import libraries to save time and increase productivity.

How can libraries help when learning Python?

Libraries act as shortcuts, providing pre-built functions and tools for common programming tasks. They allow learners to focus on solving higher-level problems rather than reinventing the wheel with basic functionalities. For example, instead of writing custom math functions, you can use NumPy, which is optimized for such tasks.

What types of Python projects should beginners start with?

Beginners should start with simple projects like cleaning messy CSVs, building a budget tracker, or creating a BMI calculator. These projects help solidify core concepts like loops, variables, and functions, and allow learners to apply their knowledge in practical, meaningful ways.

What is the importance of showing your Python projects to others?

Showing your projects is crucial because it demonstrates real proof of your skills. Instead of just claiming to know Python, having a portfolio of projects makes your abilities tangible. Sharing your work on platforms like GitHub and LinkedIn can help build credibility and open up career opportunities.

How does the speaker suggest handling the fear of criticism when sharing projects?

The speaker encourages learners to embrace imperfection and focus on showing their progress rather than waiting until projects are perfect. The key is to build a portfolio, iterate on your work, and share it with the world. They emphasize that nobody cares about perfection as much as they care about proof of skill.