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Akshay 🚀 Profile picture
@akshay_pachaar
May 10 • 12 tweets • 5 min read • Read on X
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10 great Python packages for Data Science not known to many:
1️⃣ CleanLab

You're missing out on a lot if you haven't started using Cleanlab yet!

Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.

It's like a magic wand! 🪄✨

Check this out👇
github.com/cleanlab/clean…
2️⃣ LazyPredict

A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.

Supports both regression & classification! ✨

Check this out👇
pypi.org/project/lazypr…
Image
3️⃣ Lux

A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

Check this out👇
github.com/lux-org/lux
4️⃣ PyForest

A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.

Check this out👇
pypi.org/project/pyfore…
5️⃣ PivotTableJS

PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

Check this out👇
pypi.org/project/pivott…
6️⃣ Drawdata

Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.

Very handy for learning & understanding the behaviour of ML algorithms!

Check this out👇
pypi.org/project/drawda…
7️⃣ black

The Uncompromising Code Formatter

Arguably the best, I use it everyday!!

Check this out 👇
pypi.org/project/black/
Image
8️⃣ PyCaret

An open-source, low-code machine learning library in Python that automates the machine learning workflow.

Check this out👇
github.com/pycaret/pycaret
9️⃣ PyTorch-Lightning by @LightningAI⚡️

If you like PyTorch, you'll love PyTorch Lightning!

Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.

Check this out👇
lightning.ai/docs/pytorch/s…
🔟 Streamlit

Although already quite popular but a lot of folks are yet to try this!

A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.

Check this out👇
streamlit.io
If you interested in:

- Python 🐍
- Machine Learning 🤖
- AI Engineering ⚙️

Find me → @akshay_pachaar ✔️
My weekly Newsletter on AI Engineering, Join 9k+ readers: @ML_Spring

Cheers! 🥂

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More from @akshay_pachaar

Akshay 🚀 Profile picture
May 8
How LLMs work, clearly explained:
Before diving into LLMs, we must understand conditional probability.

Let's consider a population of 14 individuals:

- Some of them like Tennis 🎾
- Some like Football ⚽️
- A few like both 🎾 ⚽️
- And few like none

Here's how it looks 👇 Image
So what is Conditional probability ⁉️

It's a measure of the probability of an event given that another event has occurred.

If the events are A and B, we denote this as P(A|B).

This reads as "probability of A given B"

Check this illustration 👇 Image
Read 10 tweets
Akshay 🚀 Profile picture
May 7
Let's build a 100% local RAG app, featuring ⌘R, a self-hosted vector
database, a fast embedding library & a reranker:
Before we begin, take a look at what we're building today!

And here's what you'll learn:

- @Ollama for serving ⌘R
- @Streamlit for building the UI
- @Llama_Index for orchestration
- @qdrant_engine to self-host a vector db
- @LightningAI for development & hosting

Let's go! 🚀
The architecture diagram presented below illustrates some of the key components & how they interact with each other!

What's new❓

- Self hosting a vector db
- Faster embedding
- Using a reranker

It will be followed by detailed descriptions & code for each component: Image
Read 12 tweets
Akshay 🚀 Profile picture
May 6
Python *args & **kwargs clearly explained:
*args allows you to pass a variable number of non-keyword arguments to a function.

It collects all non-keyword arguments passed to the function and stores them as a tuple.

Consider the following example: Image
Similarly, **kwargs allows you to pass a variable number of keyword arguments to a function.

It collects all keyword arguments passed to the function and stores them as a dictionary.

Consider the following example: Image
Read 6 tweets
Akshay 🚀 Profile picture
May 4
I have been coding in Python for 8 years now. ⏳

If I were to start over today, here's a roadmap...👇 Image
1️⃣ freeCodeCamp

4 hours Python bootcamp!!

What you'll learn:
- Installing Python
- Setting up an IDE
- Basics Syntax
- Variables & Datatypes
- Looping in Python
- Exception handling
- Modules & pip
- Mini hands-on projects 🔥

Check this out 👇
2️⃣ CS50p: Harvard University

There isn't a better place to learn #Python than @davidjmalan 's CS50p.

Beautiful explanations and great projects.
It's a complete package.

Highly recommended!!

Check this out 👇
cs50.harvard.edu/python/2022/
Read 6 tweets
Akshay 🚀 Profile picture
May 2
Lambda functions in Python clearly explained:
What are lambda functions ?

Simply put, they are small anonymous functions that are defined without a name.

Check out the syntax 👇 Image
Lambda functions can have any number of arguments, but they can only have one expression.

The expression is executed and the result is returned.

Here is an example of a lambda function that adds two numbers 👇 Image
Read 6 tweets
Akshay 🚀 Profile picture
Apr 29
Self-attention in transformers clearly explained:
Before we start a quick primer on tokenization!

Raw text → Tokenization → Embedding → Model

Embedding is a meaningful representation of each token (roughly a word) using a bunch of numbers.

This embedding is what we provide as an input to our language models.

Check this👇 Image
The core idea of Language modelling is to understand the structure and patterns within language.

By modeling the relationships between words (tokens) in a sentence, we can capture the context and meaning of the text. Image
Read 9 tweets

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