All of this is based on my experience of successfully teaching 300+ students how to code using Python.
(2 / 19)
How to learn Python for machine learning. 🐍
The beginner's guide.
(based on my real life experience of mentoring 300+ students)
🧵👇
All of this is based on my experience of successfully teaching 300+ students how to code using Python.
(2 / 19)
Because of 2 reasons:
- Comparatively easier to learn than other languages
- Has the biggest and most mature community
This makes Python a no-brainer to learn for machine learning as a beginner.
(3 / 19)
- Basic terminal commands
- Basic arithmetic (+,-,/,*)
- Accepting user input
- For & While loops
- Exception handling
- If-Else statements
- Functions, modules & Imports
(4 / 19)
- Object oriented programming in Python:Classes, Objects, Methods
- PIP (Pypi)
- List slicing
- String formatting
- Dictionaries & Tuples
- Managing environments
- Dunder methods like __init__
(5 / 19)
- Lambda functions
- Built in libraries like CSV, requests, Sqlite
- Map and Filter
- *args and **kwargs
- Async
- Decorators
(6 / 19)
Let's tackle this issue.
(7 / 19)
- Anaconda for managing environments (different versions of Python)
- Python3
- Machine learning packages like Sckit learn and TensorFlow using pip when needed
(8 / 19)
MacOS: 🔗docs.anaconda.com/anaconda/install/mac-os/
Windows: 🔗docs.anaconda.com/anaconda/install/windows/
Linux: 🔗docs.anaconda.com/anaconda/install/linux/
(9 / 19)
Here'a a guide with step by step instructions which will help you.
🔗bit.ly/3rbDoyl
(10 / 19)
Here are some popular ones
- VS code: Feature rich
- Sublime: Light and simple
- Jupyter: Useful for prototyping
- Pycharm: Full blown IDE i.e has loads of features.
(11 / 19)
These are online editors which have everything setup for you.
Not to mention colab and kaggle notebooks give you a free GPU for your machine learning workloads.
(12 / 19)
Collab : 🔗colab.research.google.com
Kaggle Notebooks : 🔗kaggle.com/notebooks/welcome
Repl : 🔗repl. it
(13 / 19)
Basics: 🔗youtube.com/watch?v=rfscVS0vtbw
Intermediate: 🔗youtube.com/watch?v=HGOBQPFzWKo
(14 / 19)
Here's a brilliant website which gives you an overview of the windows command prompt, enough for you to get started.
🔗bit.ly/34tmnGd
(15 / 19)
🔗youtube.com/watch?v=oxuRxtrO2Ag
(16 / 19)
More from Pratham Prasoon
Do you want to learn the maths for machine learning but don't know where to start?
This thread is for you.
🧵👇
The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.
I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Before diving into maths, I suggest first having solid programming skills in Python.
Read this thread for more
These are topics of math you'll have to focus on for machine learning👇
- Trigonometry & Algebra
These are the main pre-requisites for other topics on this list.
(There are other pre-requites but these are the most common)
- Linear Algebra
To manipulate and represent data.
- Calculus
To train and optimize your machine learning model, this is very important.
This thread is for you.
🧵👇
The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.
I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Before diving into maths, I suggest first having solid programming skills in Python.
Read this thread for more
Are you planning to learn Python for machine learning this year?
— Pratham Prasoon (@PrasoonPratham) February 13, 2021
Here's everything you need to get started.
\U0001f9f5\U0001f447
These are topics of math you'll have to focus on for machine learning👇
- Trigonometry & Algebra
These are the main pre-requisites for other topics on this list.
(There are other pre-requites but these are the most common)
- Linear Algebra
To manipulate and represent data.
- Calculus
To train and optimize your machine learning model, this is very important.
More from Machine learning
With hard work and determination, anyone can learn to code.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
Here’s a list of my favorites resources if you’re learning to code in 2021.
👇
1. freeCodeCamp.
I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things
2. https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:
1) You can see the most upvoted solutions so you can read really good code
2) You can ask questions in the discussion section if you're stuck, and people often answer. Free
3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE
On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.
4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.
Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.
Starting a new project using #Angular? Here is a list of all the stuff i use to launch my projects the fastest i can.
A THREAD 👇
Have you heard about Monorepo? I created one with all my Angular (and Nest) projects using https://t.co/aY5llDtXg8.
I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.
Everyone in the Angular community knows about https://t.co/kDnunQZnxE. It's not the most beautiful component library out there, but it's good and easy to work with.
There's a bunch of state management solutions for Angular, but https://t.co/RJwpn74Qev is by far my favorite.
There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics
Are you not using custom schematics yet? Take a look at this:
https://t.co/iLrIaHVafm
https://t.co/3382Tn2k7C
You can automate all the boilerplate with hundreds of files associates with creating a new feature.
A THREAD 👇
Have you heard about Monorepo? I created one with all my Angular (and Nest) projects using https://t.co/aY5llDtXg8.
I can share A LOT of code with it. Ex: Everytime i start a new project, i just need to import an Auth lib, that i created, and all Auth related stuff is set up.
Everyone in the Angular community knows about https://t.co/kDnunQZnxE. It's not the most beautiful component library out there, but it's good and easy to work with.
There's a bunch of state management solutions for Angular, but https://t.co/RJwpn74Qev is by far my favorite.
There's a lot of boilerplate, but you can solve this with the built-in schematics and/or with your own schematics
Are you not using custom schematics yet? Take a look at this:
https://t.co/iLrIaHVafm
https://t.co/3382Tn2k7C
You can automate all the boilerplate with hundreds of files associates with creating a new feature.
You May Also Like
These 10 threads will teach you more than reading 100 books
Five billionaires share their top lessons on startups, life and entrepreneurship (1/10)
10 competitive advantages that will trump talent (2/10)
Some harsh truths you probably don’t want to hear (3/10)
10 significant lies you’re told about the world (4/10)
Five billionaires share their top lessons on startups, life and entrepreneurship (1/10)
I interviewed 5 billionaires this week
— GREG ISENBERG (@gregisenberg) January 23, 2021
I asked them to share their lessons learned on startups, life and entrepreneurship:
Here's what they told me:
10 competitive advantages that will trump talent (2/10)
To outperform, you need serious competitive advantages.
— Sahil Bloom (@SahilBloom) March 20, 2021
But contrary to what you have been told, most of them don't require talent.
10 competitive advantages that you can start developing today:
Some harsh truths you probably don’t want to hear (3/10)
I\u2019ve gotten a lot of bad advice in my career and I see even more of it here on Twitter.
— Nick Huber (@sweatystartup) January 3, 2021
Time for a stiff drink and some truth you probably dont want to hear.
\U0001f447\U0001f447
10 significant lies you’re told about the world (4/10)
THREAD: 10 significant lies you're told about the world.
— Julian Shapiro (@Julian) January 9, 2021
On startups, writing, and your career:
1/ Some initial thoughts on personal moats:
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
I'm increasingly interested in the idea of "personal moats" in the context of careers.
— Erik Torenberg (@eriktorenberg) November 22, 2018
Moats should be:
- Hard to learn and hard to do (but perhaps easier for you)
- Skills that are rare and valuable
- Legible
- Compounding over time
- Unique to your own talents & interests https://t.co/bB3k1YcH5b
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
People talk about \u201cpassive income\u201d a lot but not about \u201cpassive social capital\u201d or \u201cpassive networking\u201d or \u201cpassive knowledge gaining\u201d but that\u2019s what you can architect if you have a thing and it grows over time without intensive constant effort to sustain it
— Andrew Chen (@andrewchen) November 22, 2018
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
Things that look like moats but likely aren\u2019t or may fade:
— Erik Torenberg (@eriktorenberg) November 22, 2018
- Proprietary networks
- Being something other than one of the best at any tournament style-game
- Many "awards"
- Twitter followers or general reach without "respect"
- Anything that depends on information asymmetry https://t.co/abjxesVIh9
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.