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Released Real-Time Voice Cloning utility

Which clones speech and reproduces any phrases with your intonation in just a few seconds of recording.

It runs on #Python, generates speech in real time, and is completely local, no clouds or restrictions. 🫠

🌟 GitHub: https://github.com/CorentinJ/Real-Time-Voice-Cloning

👉  https://t.me/CodeProgrammer

📝 Gradient Boosting for Regression Notes

✔️ In these notes, you will learn when to use gradient boosting, why it is a more cost-effective choice, and how to implement it correctly; so that your predictions are more accurate and your decisions more professional.

https://t.me/CodeProgrammer 💚

Want to create SQL databases visually? 🔥

Try this online tool that allows you to design and model databases using a convenient drag-and-drop interface.

It helps reinforce SQL knowledge, better understand relationships between tables, and work without installing software or registering.

The tool is completely free and open source, and also supports importing and exporting SQL code.

website: https://www.drawdb.app/


👉 https://t.me/CodeProgrammer

💯 Use Kaggle like a pro with this method!


👨🏻‍💻 Never underestimate Kaggle! One of the best ways to start learning data science and ML is Kaggle. A place where theory turns into practice, beginners become professionals, and skills turn into value.


🎯 This roadmap is the key to practical use of this amazing platform:👇


⬅️ Step one: Strengthen your basic skills!

✏️ Start with Kaggle's short and free courses. Practical, focused, and suitable for beginners.


✅ Python ⬅️ Link

☑️ Introduction to Machine Learning ⬅️ Link

✔️ Introduction to Deep Learning ⬅️ Link

✔️ Introduction to SQL ⬅️ Link

✔️ Introduction to Game AI and RL ⬅️ Link


📝 Complete list of courses ⬅️Link

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⬅️ Step two: Apply what you’ve learned.

✏️ Learning alone is not enough; you have to solve problems! Kaggle competitions are the best place for this.

✅ Classification problem for beginners

☑️ Regression-based challenge

✔️ Fake news detection with NLP

✔️ Deep learning on image data with TPU


📝 Complete list of competitions ⬅️Link

https://t.me/CodeProgrammer 🌟

Visualization of Python objects and references

Many beginner Python developers face confusion when working with mutability and references between variables. It is especially difficult to understand during debugging of complex data structures when it is unclear how exactly they are connected. ⌨️

So here is memory_graph — an open-source tool for visualizing Python objects and references. It shows the data structure, call stack, and connections between variables.

👉 Link: https://github.com/bterwijn/memory_graph?tab=readme-ov-file

It also supports working with recursion and structures such as binary trees or linked lists.

Works in VS Code, Jupyter, PyCharm, and is available online without installation.

😁 more: https://memory-graph.com/#breakpoints=8&continues=1&timestep=1.0&play

👉 https://t.me/CodeProgrammer

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👇
https://lnkd.in/dY2fp5YW

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👇
https://lnkd.in/ggZ-HByv

3️⃣ Lux
A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

Check this out👇
https://lnkd.in/genaV395

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👇
https://lnkd.in/gv2tsjMe

5️⃣ PivotTableJS
PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

Check this out👇
https://lnkd.in/dapGg-AS

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👇
https://lnkd.in/gBSrQ-e4

7️⃣ black
The Uncompromising Code Formatter
Arguably the best, I use it everyday!!

Check this out 👇
https://lnkd.in/gsz2Mxqn

8️⃣ PyCaret
An open-source, low-code machine learning library in Python that automates the machine learning workflow.

Check this out👇
https://lnkd.in/gUzh-YZM

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👇
https://lnkd.in/dir2Xej7

🔟 Reflex
Build Performant, customizable web apps in pure Python that you can deploy in seconds
Reflex is a library to build full-stack web apps in pure Python.

Check this out👇
https://lnkd.in/gz_vsNba

https://t.me/CodeProgrammer 🆘

🏳️‍🌈 Learning Python for science is
✅ with these 8 awesome GitHub repos!


🖥 Repo: Project Based Learning

💬 One of the most famous educational repos with 230K+ stars that implements various algorithms and projects using Python.

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🖥 Repo: Real Python Materials

💬 Supplementary resources and exercises including project-based tutorials, guides, and practical exercises.

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🖥 Repo: Learn By Doing

💬 Project-based tutorials in AI and machine learning for all levels.

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🖥 Repo: Awesome Jupyter

💬 A curated collection of notebooks, tools, and powerful libraries for working with Jupyter.

➖ ➖ ➖

🖥 Repo: Python Mini Projects

💬 A collection of mini-projects like games and small apps that you can quickly run and practice.

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🖥 Repo: 100Projects of Code

💬 An educational challenge including 100 real projects; you practice and see your progress day by day.

➖ ➖ ➖

🖥 Repo: Data Science Projects

💬 Practical ideas and examples to start data science with Python.

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🖥 Repo: Python Project Scripts

💬 Small and large scripting projects, from beginner to advanced levels.

By: https://t.me/CodeProgrammer ✈️