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. ЁЯРН
The beginner's guide.
(based on my real life experience of mentoring 300+ students)
ЁЯз╡ЁЯСЗ
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: ЁЯФЧdocs.тАЛanaconda.тАЛcom/anaconda/install/mac-os/
Windows: ЁЯФЧdocs.тАЛanaconda.тАЛcom/anaconda/install/windows/
Linux: ЁЯФЧdocs.тАЛanaconda.тАЛcom/anaconda/install/linux/
(9 / 19)
Here'a a guide with step by step instructions which will help you.
ЁЯФЧbit.тАЛly/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.тАЛe 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 : ЁЯФЧcolab.тАЛresearch.тАЛgoogle.тАЛcom
Kaggle Notebooks : ЁЯФЧkaggle.тАЛcom/notebooks/welcome
Repl : ЁЯФЧrepl. it
(13 / 19)
Basics: ЁЯФЧyoutube.тАЛcom/watch?v=rfscVS0vtbw
Intermediate: ЁЯФЧyoutube.тАЛcom/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.
ЁЯФЧbit.тАЛтАЛly/34tmnGd
(15 / 19)
тАЛЁЯФЧyoutube.тАЛcom/watch?v=oxuRxtrO2Ag
(16 / 19)
More from Pratham Prasoon
More from Machine learning
Really enjoyed digging into recent innovations in the football analytics industry.
>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut ЁЯЩЗтАНтЩВя╕П https://t.co/9YOSrl8TdN
For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.
This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5
Bit of this that I nerded out on the most is "ghosting" тАФ technique used by @counterattack9 & co @stats_insights, among others.
Deep learning models predict how specific players тАФ operating w/in specific setups тАФ will move & execute actions. A paper here: https://t.co/9qrKvJ70EN
So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut ЁЯЩЗтАНтЩВя╕П https://t.co/9YOSrl8TdN
For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.
This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5
Bit of this that I nerded out on the most is "ghosting" тАФ technique used by @counterattack9 & co @stats_insights, among others.
Deep learning models predict how specific players тАФ operating w/in specific setups тАФ will move & execute actions. A paper here: https://t.co/9qrKvJ70EN
So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?
Do you want to learn the maths for machine learning but don't know where to start?
This thread is for you.
ЁЯз╡ЁЯСЗ
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 learningЁЯСЗ
- 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.
ЁЯз╡ЁЯСЗ
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 learningЁЯСЗ
- 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.
