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Sumanth

@Sumanth_077

Jun 30 • 5 tweets • 2 min read • Read on X

I've been coding in Python for 4 years now.

If I were to start over again in 2024, here's a roadmap:

1. CS50’s Introduction to Programming with Python:

The course is designed for students with or without prior programming experience who’d like to learn Python specifically.

Check this out:

cs50.harvard.edu/python/2022/

2. Corey Schafer

Best YouTube channel to learn Python!

Corey explains every pythonconcept from scratch along with project based implementation.

Check it out:

youtube.com/@coreyms

3. Project-Based Learning:

Learning the concepts is great, but implementing them for real-world use cases will get your hands dirty.

Check out the below repo.

Also, leverage LLMs like ChatGPT and Claude 3.5 Sonnet to learn and build things quickly.

github.com/practical-tuto…

That's a wrap!

If you are interested in any of these topics:

- Python 🐍
- Data Science 📈
- Machine Learning 🤖
- Data Analysis 📊
- LLMs 🧠
- MLOps 🛠

Find me → @Sumanth_077 ✅

I'm sharing daily content over here.

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More from @Sumanth_077

Sumanth

@Sumanth_077

Jun 12

5 github repositories you should definitely check as a Machine Learning Engineer:

1. Deep Learning Drizzle

This is an organized website where you can find all the FREE and best courses in:

→ Machine Learning
→ NLP
→ Computer Vision
→ Reinforcement Learning from Top Universities (all in one place)
github.com/kmario23/deep-…

2. Made with ML

Best Repository to Learn MLOps.

Also help you learn the foundations of machine learning through intuitive explanations, clean code and visualizations.
github.com/GokuMohandas/M…

Read 7 tweets

Sumanth

@Sumanth_077

Jun 9

7 amazing Python libraries for Data Science and Machine Learning not known to many:

1. Bashplotlib

Terminal Version of Matplotlib for Data Visualization.

Bashplotlib helps you to create visual charts right from your terminal.

pypi.org/project/bashpl…

2. Pandas AI

Pandas AI adds Generative AI capabilities to Pandas.

Now you can analyze complex data frames and plot visualizations just by using natural language.

github.com/Sinaptik-AI/pa…

Read 9 tweets

Sumanth

@Sumanth_077

Jun 3

Princiapal Component Analysis (PCA) clearly explained and implemented from scratch in Python:

First Import numpy and define a class named "PCA" to encapsulate the operations.

In the __init__ constructor let's initialize the number of components to reduce to, which is n_components, and similarly create placeholders for mean and eigenvectors.

Now define the "fit" method that takes in that data and calculates the mean, normalizes the data, computes the covariance matrix, eigenvalues, and eigenvectors.

Then the Eigenvectors are sorted based on eigenvalues.

You may ask how, eigen vectors does dimensionality reduction?

Read 6 tweets

Sumanth

@Sumanth_077

May 29

Python library that adds Generative AI capabilities to Pandas

Introducing PandasAI, now you can analyze complex data frames and plot visualizations just by using natural language:

With Pandas AI you can:

→ Clean the Data

→ Impute missing values

→ Generate New features

→ Analyze and manipulate the data

Not just that you can also ask it to plot the charts:

Here is plotting Histogram with Pandas AI:

Checkout the Repository here:
github.com/gventuri/panda…

Read 4 tweets

Sumanth

@Sumanth_077

May 2

Neural Network implemented from scratch in Python (step-by-step explanation with code):

Below is the simple Neural Network consists of 2 layers:

- Hidden Layer
- Output Layer

First Initialize the size of layers along with the weights & biases.

And also define the sigmoid activation function & it's derivative which is really key to introduce non-linearity.

Forward Pass:

Here the input data is passed through the neural network to obtain the predicted output.

In forward pass, First calculate the output of the hidden layer.

hidden_output = X•W1 + b1

Then apply the sigmoid activation to the output.

output = sigmoid( (X•W1) + b1)

Read 9 tweets

Sumanth

@Sumanth_077

Apr 27

Train and Evaluate all your Machine Learning Models at once!

Introducing Lazy Predict a Python Library that allows you to train, test and evaluate all your ML models at once with just a few lines of code.

Model selection is key for any ML Project which includes identify the model that generalizes well and provides the best performance.

This library can save time and effort by training and evaluating all your ML models at once.

Here is using LazyRegressor for Regression Problems:

Similarly here is using LazyClassifier for Classification Problems.

Check this out:👇

Read 5 tweets

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