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
More from Machine learning
This is a Twitter series on #FoundationsOfML.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
Thanks for this incredibly helpful analysis @dgurdasani1
Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published
Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?
Not to mention the efficacy data of the full first dose over that same period?
I’d quite like to know whether I am to be a guinea pig & the ongoing risks to manage
You attached photos of excerpts from a paper. Could you attach the link?
Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.
Here’s the link to the
I’ve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.
Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published
SUMMARY: the Oxford/Astra trial examined dosing with gaps between 4-12 wks- although longer gaps appear to be limited mostly to younger participants. There was no difference reported in published data between these & efficacy from the 1st dose seems high for severe disease.
— Deepti Gurdasani (@dgurdasani1) December 31, 2020
Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?
Not to mention the efficacy data of the full first dose over that same period?
I’d quite like to know whether I am to be a guinea pig & the ongoing risks to manage
You attached photos of excerpts from a paper. Could you attach the link?
Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.
Discussions of 1 vs 2 doses suggest many are not aware of Pfizer's trials which evaluated 1 vs 2 dose immunogenicity, assessed multiple formulations (BNT162b1 BNT162b2 etc) & conducted dose-ranging in both young & old adults at the start. Saw "clear benefit of booster at day 21" pic.twitter.com/mpyxu9xFSF
— Dr Nicole E Basta (@IDEpiPhD) December 31, 2020
Here’s the link to the
I’ve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.
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Athlete\u2019s body.
Artist\u2019s soul.
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When you choose who to follow on Twitter, you are choosing your future thoughts.
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Working on a problem reduces the fear of it.
— James Clear (@JamesClear) August 30, 2020
It\u2019s hard to fear a problem when you are making progress on it\u2014even if progress is imperfect and slow.
Action relieves anxiety.
https://t.co/A7XCU5fC2m
We often avoid taking action because we think "I need to learn more," but the best way to learn is often by taking action.
— James Clear (@JamesClear) September 23, 2020
