Best Python libraries for Machine Learning that are open source ๐Ÿ’ฏ

A summary about each with their GitHub link ๐Ÿ˜‰

Thread ๐Ÿงต๐Ÿ‘‡

1. Pandas

It aims to be the fundamental high-level building block for practical, real-world data analysis in Python.

๐Ÿ”— https://t.co/toOkhEazmQ
2. OpenCV

It has more than 2500 highly optimised algorithms for machine learning and computer vision that can do just about anything with images.

๐Ÿ”— https://t.co/Z967hfrsvI
3. Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

๐Ÿ”— https://t.co/ocpMxzCSmX
4. Tensorflow

It's an end-to-end Machine Learning and Deep Learning library to solve real-world challenges.

๐Ÿ”—https://t.co/wNhupEWq3k
5. Keras

Released in 2015, Keras is an advanced open-source Python deep learning API and framework built on top of Tensorflow-another powerful ML platform.

๐Ÿ”— https://t.co/hHDCRRHrB7
6. NumPy

It is the fundamental package for scientific computing with Python.

๐Ÿ”— https://t.co/SLIqjhdQNX
7. Scikit-learn

It is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

๐Ÿ”— https://t.co/nF6JHcO6TF
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Simple and effective way 2 make Money


Idea 1:- Use pivot level like 14800 in case of nifty and sell 14800straddle monthly expiry (365+335) exit if nifty closes on daily basis below S1 or above R1

After closing below S1 if it closes above S1 next day or any day enter the same position again vice versa for R1

Idea2:- Use R1 and S1 corresponding strikes multiple
Incase of R1 15337 take 15300ce
N in case of S1 14221 use 14200pe
Sell both and hold till expiry or exit if nifty closes below S1 or above R1 around closing
If the same bounces above S1 and falls below R1 re-enfer same strikes

Use same criteria for nifty, usdinr and banknifty

(This is must)Use this margin rule for 1lot banknifty pair keep 4Lax margin
For nifty one lot keep 3Lax
For usdinr 100lots keep 4Lax

I bet you if you do this on consistent basis your ROI will be more than 70% on yearly basis.

Couldn't explain easier than this

Criticisms are most welcomed.