How I Would Learn Python FAST in 2024 (if I could start over)

Summary

This video offers a comprehensive guide for beginners to learn Python efficiently. It addresses the relevance of Python in the AI era, emphasizing its demand and the ability to build applications with AI. The speaker suggests starting with an online editor like Repl.it or Google Collab, then progressing to local development with tools like Visual Studio Code. A mind map outlines learning stages from basics to advanced topics. The video advocates for learning by doing, creating projects, and teaching others to solidify knowledge, overcoming the 'valley of despair' in the learning process. It also mentions using AI tools like GitHub Copilot to enhance coding efficiency.

Takeaways

• 🐍 Learning Python is still highly valuable in the era of AI, as it empowers you to build applications on top of AI models.
• 🚀 You can reach an intermediate level in Python within 3 months or less if you learn it the right way.
• 💡 Python's demand remains high, and proficiency in it helps you verify and correct AI-generated code.
• 👨‍💻 Choosing the right development environment depends on your goals, whether for development, data analysis, or AI.
• 🌐 Online code editors like Repl.it and hosted services like Google Colab are good starting points for learning Python.
• 💻 For long-term learning, installing Python locally and using an integrated development environment (IDE) like Visual Studio Code is recommended.
• 📚 The basics of Python include understanding data types, variables, functions, and control flows like conditional statements and loops.
• 📈 Intermediate Python learning involves working with files, version control, data science libraries, and object-oriented programming.
• 🛠️ Good coding practices are crucial, including following style guides, using meaningful variable names, and writing clean, efficient code.
• 🏆 Advanced Python learning allows you to build complex applications, use APIs, develop web apps, and deploy applications on cloud platforms.
• 🔄 The best way to learn is through a cycle of learning, doing, and teaching, which helps solidify knowledge and reveal gaps in understanding.

Q & A

Why is Python still worth learning despite the rise of AI?

Python remains one of the most in-demand programming languages and is essential for building applications on top of AI models. It allows users to be creators rather than just consumers of AI, and it's crucial for verifying and correcting AI-generated code.

What are the potential risks of relying solely on AI-generated code?

AI-generated code may contain errors or 'hallucinations' that can be harmless in other contexts but could be fatal in coding, such as inadvertently installing malware.

What is the recommended approach to learning Python if starting from scratch?

The recommended approach is to focus on learning the basics first, such as variables, data types, and functions, and then gradually move to more advanced topics like data science, machine learning, and object-oriented programming.

What are some beginner-friendly development environments for Python?

For beginners, online code editors like Repl.it or hosted services like Google Colab are recommended as they require no setup and provide a user-friendly environment to start coding immediately.

Why is it beneficial to learn Python for data science and machine learning?

Python is widely used in data science and machine learning due to its simplicity and the powerful libraries available like NumPy, pandas, Matplotlib, and scikit-learn, which facilitate data analysis and machine learning tasks.

What are some good coding practices that should be followed when learning Python?

Good coding practices include following the Python style guide, using meaningful variable names, avoiding hard coding, utilizing list comprehensions and generators, adding error handling, commenting and documenting code, and using virtual environments for package management.

How can one avoid getting overwhelmed while learning Python?

To avoid feeling overwhelmed, focus on learning with a purpose, such as solving a specific problem through a personal project. This helps shift the focus from perceived incompetence to learning what is necessary to solve real-life problems.

What is the significance of teaching what you've learned in Python?

Teaching what you've learned helps solidify your understanding, reveals gaps in knowledge, and deepens your learning. It's a powerful way to reinforce concepts and improve problem-solving skills.

How can GitHub Copilot assist in Python development?

GitHub Copilot can assist by writing code faster and reducing errors, allowing developers to focus more on the creative process and idea development rather than getting bogged down with syntax or data type issues.

What is the 'Dunning-Kruger effect' mentioned in the script, and how does it relate to learning Python?

The Dunning-Kruger effect is a cognitive bias where people with low ability at a task overestimate their ability. In the context of learning Python, beginners might feel they are making rapid progress initially but then hit a point where they realize how much they still need to learn, which can lead to a dip in confidence and motivation.

Outlines

00:00

🐍 Starting Your Python Journey

The speaker shares their personal experience with learning Python, which began seven years ago as a data analyst. They emphasize that while it took them three years to become proficient, today's resources allow one to reach an intermediate level in Python within three months. The video aims to guide viewers on how to learn Python effectively from scratch. The speaker addresses the concern of whether Python is still relevant in the age of AI, arguing that Python remains a highly demanded language and is essential for building applications with AI models. They also caution against relying solely on AI-generated code due to potential security risks, such as malware installation. The importance of understanding programming logic is highlighted, and the speaker advises not to worry too much about choosing a development environment initially, suggesting that the choice depends on whether one wants to become a developer or focus on data analysis, machine learning, or AI.

05:01

💻 Choosing the Right Development Environment

The speaker discusses the importance of selecting a suitable development environment for learning Python, whether it's for development, data analysis, or AI. They mention the option of using online code editors like Repl.it for immediate coding without setup or Jupyter Notebook for data science, which allows running code blocks individually. Google Colab is recommended for its ease of use and free computing resources. However, for long-term learning and local project development, the speaker suggests installing Python on one's computer and using an integrated development environment (IDE) like Visual Studio Code or PyCharm. The speaker also introduces a mind map for learning Python, dividing topics into basic, intermediate, and advanced levels, and encourages viewers to focus on the basics first before moving on to more advanced topics.

10:03

🌟 Advancing in Python and Overcoming Challenges

The speaker provides advice on advancing in Python, emphasizing good coding practices such as following style guides, using meaningful variable names, and avoiding hardcoded values. They suggest using list comprehensions and generators for efficiency and incorporating error handling and commenting in the code. The speaker also touches on the importance of using virtual environments and unit testing. They recommend focusing on specific areas depending on one's goals, such as mastering data science packages like NumPy, pandas, Matplotlib, and scikit-learn for data science, or learning object-oriented programming and efficient coding for software engineering. The speaker introduces CodeCrafters, a learning platform for engineers, and encourages viewers to use the provided mind map as a reference for building a personalized learning curriculum. The speaker stresses the importance of learning by doing and teaching, suggesting that creating something useful or teaching others what one has learned can solidify knowledge and reveal gaps in understanding. They also discuss the 'dunning-kruger' effect, where beginners may overestimate their abilities, and the common dip in motivation that occurs when learning a new skill. The speaker advises finding a purpose and starting with a simple personal project to maintain motivation and build problem-solving skills, also mentioning the use of AI tools like GitHub Copilot to assist in the coding process.

Keywords

💡Python

Python is a high-level, interpreted programming language known for its readability and ease of use. In the video, it is presented as a valuable skill to learn, especially for those interested in data analysis, machine learning, and AI. The speaker suggests that Python's syntax, which resembles plain English, makes it accessible even to beginners, and it remains in high demand despite the rise of AI.

💡Development Environment

A development environment refers to the setting in which code is written, tested, and debugged. The video discusses the importance of choosing the right environment based on one's goals, whether it's for general development or specialized tasks like data analysis. Examples mentioned include local code editors and hosted services, with specific tools like Visual Studio Code and Jupyter Notebook being highlighted.

💡Integrated Development Environment (IDE)

An IDE is a software application that provides comprehensive facilities for coding, such as intelligent code completion, debugging, and version control. In the context of the video, the speaker recommends using an IDE like Visual Studio Code for its convenience in editing code and managing project functionalities, which is crucial for efficient programming.

💡Data Analysis

Data analysis involves examining, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. The video emphasizes learning Python for data analysis, suggesting that it's a powerful tool for handling data with libraries like NumPy, pandas, and Matplotlib, which are essential for tasks such as data manipulation and visualization.

💡Machine Learning

Machine learning is a subset of AI that focuses on the development of algorithms and models that enable computers to learn and make decisions from data. The video touches on the necessity of mastering Python for machine learning, as it is widely used in this field due to its simplicity and the availability of libraries like scikit-learn that facilitate the creation of machine learning models.

💡Artificial Intelligence (AI)

AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video discusses the role of Python in AI, noting that while AI can generate code, understanding Python is crucial for building applications on top of AI models and for verifying and correcting AI-generated code.

💡Version Control

Version control is a system that records changes to a file or set of files over time so that specific versions can be recalled later. The video mentions the importance of learning Git, a version control system, for managing code changes, especially when working on collaborative projects or maintaining a history of code revisions.

💡Object-Oriented Programming (OOP)

OOP is a programming paradigm based on the concept of 'objects', which can contain data in the form of fields (often known as attributes or properties) and code in the form of procedures (often known as methods). The video suggests that learning OOP in Python is essential for creating flexible and reusable code, which is a key aspect of software development.

💡Decorators

In Python, a decorator is a function that takes another function and extends its behavior without explicitly modifying it. The video briefly mentions decorators as an advanced topic in Python, which are used to add new functionality to existing functions in a clean and maintainable way, often used in web development and other advanced programming tasks.

💡Coding Practices

Coding practices refer to the conventions and methodologies followed by programmers to write efficient, readable, and maintainable code. The video emphasizes good coding practices, such as following style guides, using meaningful variable names, and writing clean and efficient code, as these are critical for professional development and collaboration in the real world.

💡GitHub Copilot

GitHub Copilot is an AI-powered code assistant that helps developers write code more quickly and with fewer errors. The video mentions using GitHub Copilot to enhance the coding process, particularly in data science, by focusing on the creative aspects rather than getting bogged down with technical details, which can significantly improve productivity.

Highlights

Learning Python can be accelerated to an intermediate level in 3 months with the right approach.

Python remains a highly demanded programming language, even in the era of AI.

Proficiency in Python allows you to build applications on top of AI models, not just consume AI.

AI-generated code can have significant limitations, such as the potential to install malware.

Python's readability, resembling plain English, is beneficial for beginners.

Choosing the right development environment depends on whether you aim to develop or analyze data.

Online code editors like Repl.it and hosted services like Google Colab are good starting points.

For long-term learning, installing Python locally and using an IDE like Visual Studio Code is recommended.

Learning the basics of Python includes understanding variables, data types, functions, and control flows.

A mind map for learning Python is provided, dividing topics into basic, intermediate, and advanced levels.

Intermediate Python learning involves working with files, version control, data science, and object-oriented programming.

Good coding practices are emphasized, including following style guides and writing clean, efficient code.

For data science and machine learning, mastering basic Python packages like NumPy, pandas, and scikit-learn is crucial.

Advanced Python learning includes building complex applications, using APIs, and web development with frameworks like Django and Flask.

Code Crafters, a learning platform for engineers, is mentioned as a resource for advanced Python learning.

The best way to learn is by doing, and reinforcing knowledge by teaching others.

The 'Dunning-Kruger effect' is discussed, highlighting the common dip in confidence when learning to code.

Finding a personal project to solve a real-life problem can help maintain motivation during the learning process.

Using AI tools like GitHub Copilot can assist in writing code more efficiently.

Transcripts

if you were to start learning to code

today where would you begin I start

learning python 7 years ago while

working as a data analyst it took me at

least 3 years to feel confident in my

skills but today things are very

different you can reach an intermediate

level in Python in just 3 months or less

if you learn it the right way in this

video I'm going to show you exactly how

I would Learn Python if I were starting

from scratch today before we dive in

let's address the elephant in the room

is it still worth learning python in the

ede of artificial intelligence what's

the point when AI can write code much

faster than we can well python is still

one of the most in demand programming

languages today in addition knowing how

to program in Python empowers you to

build applications on top of AI models

this means you're not just a consumer of

AI but you can build things with it yes

AI can now generate codes making coding

more accessible with no codes and local

tools but it still has significant

limitations that we can't ignore

hallucination in Aver ation may be

amusing and insignificant but in coding

it can be fatal a cyber security

researcher recently noticed that large

language models repeatedly produced a

command to install a non-existent python

package without sufficient understanding

you might unknowingly allow AI generated

code to install malware in your

environment that's why you still need to

know how to code even if you use AI to

write code being proficient in Python

will help you verify and correct AI

generation code and leverage AI

effectively and safely programming is

about logical thinking and using that

creatively to create a set of

instructions and it helps that python

mostly looks like plain English even if

you don't know how to code you can sort

of read it I wish I wouldn't worry too

much about choosing a development

environment to use when I first started

but anyway choosing the right one might

make your life a little bit easier which

development environment to choose

depends on your goal Do You Want To

Learn Python to become a developer or

you want to learn it to do data analysis

and machine learning or Ai and you can

also choose between a local code editor

and a hosted service at the very

beginning you may not want to bother

with installing Python and setting an

environment yourself so you can use an

online code editor such as repet where

you can start writing code right away in

an online environment for data science

and machine learning specifically a

common tool is tbit notebook which

allows you to run blocks of code

individually and inspect results and

online version of Jupiter notebook is

Google collab which is a hosted jupyter

notebook service it requires no setup to

use and provides free access to some

Computing resources which is pretty neat

if you have a small project and you want

to collaborate with others in real time

however in the long run it's best to

install python in your computer so you

can use it locally then you can use

Python directly in your terminal try

using it as a calculator or print some

fun jerk in your terminal the next step

is to install and integrate development

environment like Visual Studio code or

byarm these are software that help you

develop General applications and they

make it easy to edit code and have all

the functionalities built in that you

might need for your project hence the

name integrated development environment

I used to use Jupiter notebook a lot for

my data science project in Python but

nowadays I find it easier to use Jupiter

notebook inside Visual Studio code as I

can easily use GitHub copilot which

we'll talk about later in this video

once you decide which tool to use you

can immediately start learning the

basics like variables data types and

functions you really want to know the

basics of control flows like conditional

statements and Loops don't get carried

away a lot of times we get stuck in the

details of some small topics and lose

motivation and we want to avoid that to

help you visualize what you may want to

learn at each stage of learning python I

decide to create this mind map for

learning python if you laid out every

single python Concept in a mind map this

is basically what it would look like you

can see on this mind map I divide the

topics into basic intermediate and

advanced as you can imagine the basic

topics include the integrated

development environment for python how

to install and manage packages working

directory and all things basic just in

any programming languages such as data

types variables functions operators

conditional statements and loop

statements it's fine not to completely

understand everything at this stage you

have the chance to practice and solidify

your knowledge in the next levels moving

on to the intermediate topics where you

can start doing really useful things on

this level you should learn more

advanced things like working with

different types of files Version Control

with Git python for data science AKA

doing data analysis and machine learning

with Python objectoriented Programming

decorators debugging and arror handling

at this stage you also want to pay

attention to good coding practices

meaning writing clean read readable and

efficient code there's a distinction

between writing code for a personal toy

project and building something in the

real world some of the most important

good coding practices may include follow

the style guide for python code and be

consistent with it use meaningful

variable names trust me naming variables

is one of the hardest thing in

programming avoid hard coding numbers in

your Cates because no one would be able

to understand what these numbers

represent except yourself use list

comprehensions and generators when NE

necessory instead of using for Loops

also add Arrow handling in your code

provide commenting and documentation in

your code use Virtual environments to

encapsulate packages for separate

projects if necessary you also want to

create unit tests for your functions at

this stage there could be a lot of

things to learn so I'd recommend you

start thinking about what you want to

focus on for example if you're learning

python for data science machine learning

and AI you know that you need to master

all the basics plus some the most basic

python packages you need to use for

working with data such as numai pandas M

plot lip caborn and Cy learn while if

you want to learn to become a data

engineer or software engineer then

objectoriented programming decorators

and learn to write efficient and clean

code are even more important once you've

moved on to the more advanced topics you

can start building more complex

applications and you can start learning

how to use an API for your application

develop a web application with a user

interface or a complex game and so on

this is also where you start moving from

building prototypes in Jupiter notebooks

to building an userfriendly application

in Python there are a lot of popular

Frameworks that make it easier for you

to develop a web app for example jungo

and flask you also learn how to deploy

your application on a cloud hosting

platform if you want to learn Advanced

programming and develop complex

applications I'd like to shout out to

code Crafters who has currently

sponsored this video code Crafters is a

learning platform to help engineers get

really good at their craft build your

own redist get Docker sequel light from

scratch just for fun they also have a

separate python track where you can

learn to use Python to create your own

software this platform targets senior

programmers who want to build tools and

master their skills if this sounds

interesting to you check out code

Crafters in the description below all

right you can use this mind map as a

useful reference for knowing what is

what in Python and building your own

learning curriculum based on your goals

and your needs after that you can start

looking up some tutorials on YouTube or

signing up for an online python course

and go through the concepts you want to

learn I've also linked this mind map in

the video description below so feel free

to check it out and explore it this

overview is just a Guidance the best way

to actually learn something though is

through doing and the best way to

actually own your knowledge is through

teaching let me explain most of us learn

like this you learn something thing and

then move on to the next thing and then

move on to the next next thing by the

time you move on to the fourth or the

fifth thing you already forget the

previous things you learned sounds

familiar this is not because you're

stupid it is because if you learn

something without immediately applying

it your brain would get a signal saying

oh this thing is apparently not

important because I never need to use it

for anything so a better way to learn

python or any foreign languages is to is

to immediately put what you learned into

practice and start creating something

useful with it this something doesn't

need to be too useful for example you

just learn about python functions you

can create a function to calculate your

BMI if you need some ideas you can

simply go to chat tobt and ask it to

create a quiz or practice problem for

you at this point you might think okay

cool I can move on to the next thing or

you might decide to go to the next level

which is to teach others what you've

learned I love writing blog posts and

tutorials or making videos explain

Concepts I just learned in fact I

believe you should always teach what you

learn by teaching you learn it more

deeply and it helps reveal gaps in your

knowledge that you would otherwise never

know it said that no one learns as much

about the subject as one who is forced

to teach it so for me the best formula

to learn coding is actually learn do

teach learn do teach and so on this is

easier said than done almost all of us

experience some sort of dip in

confidence and motivation while picking

up a new skill and coding is no

exception you get started feeling very

excited learning about how to print

hello world and make the first for Loop

to count from 1 to 10 you feel like

you're crushing it and becoming a guru

very soon but this is just an illusion

this is called the duning CR effect in

Psychology which basically means the

incompetent people usually overestimate

their own abilities after one or two

weeks you start feeling overwhelmed and

realize how little you actually know

about programming You Want To Learn

Python to do machine learning but you

just realize that you also need to learn

math statistics and some computer

sciency stuff at the same time and this

is completely normal unfortunately 99%

of people give up at this stage they

cannot push themselves to step up and

keep going even when they don't feel

like doing it if you can't go past the

stage then you'll forever be a beginner

and the problem is in today's world

being a beginner you're less competent

than chbd so all you need to do is to

trust the process for me the best way to

get out of the valley of Despair is to

learn with a purpose you want to find a

problem that you want to solve and

create a personal project that solves it

this will help shift your focus from I'm

incompetent to I'm learning what it

takes to solve a real life problem you

might be asking but I'm still just a

beginner I don't know where to start a

good thing to note is that your project

doesn't need to be complicated at all it

doesn't need to change the world it just

needs to be a little bit useful one of

my projects is to take all the books

from The Witcher book series to create a

network of characters in the story it is

useful but not too useful it was fun and

doable and there were a lot of new ideas

and Concepts thrown at me and that's the

point when you get absorbed in your

project you're just too excited to stop

there were quite a few weekends when I

coded until Wei hours just to see if

something actually works and my project

slowly coming to life I think there's no

better source of motivation than this

you also come across many similar

projects from other people you can then

reverse engineer what they have done to

tackle the same challenges in the

project and this will help you build

your own problem solving skills in

addition in the age of AI tools today

you no longer have to do projects all by

yourself nowadays I often use GitHub

co-pilot to help me write code faster

and less error prone in data science

this really helps you focus more on the

idea and the creative process behind it

rather than getting bed down with fixing

the data types or adjusting access on

the chart believe me this can cost a lot

of time and by the way I'm currently

working on a course on python for data

science and AI project if you're

interested check out the link below to

be the first to hear about it when it's

available thank you for watching bye-bye