⚡️ 8 free courses to master large language models:

1- LLM University

The course provides an understanding of how LLM work, their practical applications, and guides students in using LLMs to build and deploy applications.
https://docs.cohere.com/docs/llmu

2- hugging face NLP course

This course provides comprehensive knowledge of Hugging Face transformers, datasets, tokenizers, and the Accelerate tool in the field of Natural Language Processing (NLP).
https://huggingface.co/learn/nlp-course/chapter1/1

3- DeepLearningAI

A collection of free courses created in collaboration with many companies such as LangChain, OpenAI, Google, Weights & Biases, Microsoft and others.
https://www.deeplearning.ai/short-courses/

4- Weights_biases course

This course shows how to create LLM-based applications using API, Langchain и W&B Prompts . He talks about developing, experimenting, and evaluating LLM-oriented applications.
https://www.wandb.courses/courses/building-llm-powered-apps

5- Introduction to LLMs course by google cloud

An introductory level course covering what LLMs are, their use cases, and how to improve LLM performance using prompt tuning
https://www.cloudskillsboost.google/course_templates/539

6- Databricks courses

The program includes two courses: " LLMs: Application through Production " and " LLMs: Foundation Models from the Ground Up "
https://www.databricks.com/blog/enroll-our-new-expert-led-large-language-models-llms-courses-edx

7- Course "LangChain & Vector Databases in Production" from activeloopai, towards_AI and Intel

The three-course series will provide students with the knowledge and skills to learn, fine-tune, and integrate LLM into production.
https://learn.activeloop.ai/courses/langchain

8- LLM Bootcamp

Covers topics such as Prompt Engineering, LLMOps, UX for language user interfaces, augmented language models, rapid LLM application development, future trends in LLM, fundamental concepts and walkthrough of askFSDL.
https://fullstackdeeplearning.com/llm-bootcamp/

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🖥 Hitchhiker's Guide to Python

Python Best Practices Guidebook

A guide to best practices for installing, configuring and using Python on a daily basis, including pip, numpy, virtualenv and more.

Resources: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A/110

🖥 Turn (almost) any Python command line program into a full GUI application with one line.

🔗 GitHub: https://github.com/chriskiehl/Gooey

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🔐 Lock Your Photos using Python

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⭕️ Data science learning roadmap in 2024
📂 with all videos, projects, books and pamphlets

👨🏻‍💻 Krish Naik, one of the famous Indian data scientists, with more than 845 thousand users on his YouTube channel , has published a comprehensive roadmap for learning data science in the new year.

🔷 This roadmap is excellent for all those who are planning to start learning data science or are experienced in this field and want to get acquainted with the latest training in this field.👌🏼

✅ This comprehensive roadmap includes learning from scratch Python, machine learning, deep learning, MLOPS, LLM, Generative AI and many other things.


┌ 🏷 Data Science Perfect Roadmap
└ 📝 Roadmap To Learn Data Science 2024

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🖥 DataQA extractor

App to extract structured fields into a spreadsheet from unstructured text.

DataQA extractor is a Python application for extracting structured fields from unstructured text and adding them to a spreadsheet.

GitHub: https://github.com/dataqa/extractor

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Using Haar Cascade for Object Detection

Before the deep learning revolution redefined computer vision, Haar features and Haar cascades were the tools you must not ignore for object detection. Even today, they are very useful object detectors because they are lightweight. In this post, you will learn about the Haar cascade and how it can detect objects. After completing this post, you will know:

🟢 What is Haar features
🟢 How Haar cascade is using Haar features to detect objects
🟢 Some predefined Haar cascade object detectors in OpenCV
❤️ Let’s get started.

https://machinelearningmastery.com/using-haar-cascade-for-object-detection/

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🔅 Compress Images

from PIL import Image
in_img = 'input.png'
out_img = 'compressed.png' 

# Open the image
with Image.open(in_img) as img:
    # Save the compressed image
    img.save(out_img, 'PNG', quality=80)

print(f"Image compressed successfully!")

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Deep Learning with PyTorch Cheat Sheet
Learn everything you need to know about PyTorch in this convenient cheat sheet

PyTorch is an open-source machine learning library primarily developed by Facebook's AI Research Lab (FAIR). It is widely used for various machine learning and deep learning tasks, including neural network development, natural language processing (NLP), computer vision, and reinforcement learning. In this cheat sheet, learn all the fundamentals of working with PyTorch in one convenient location!

Download 👇👇👇

Deep Learning with PyTorch Cheat Sheet

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Python Cheat Sheet for Beginners

Python is the most popular programming language in data science. Use this cheat sheet to jumpstart your Python learning journey.

Download 👇👇👇

Python Cheat Sheet for Beginners

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DeepFake Detection using Convolutional Neural Networks✅🆕

https://techvidvan.com/tutorials/deepfake-detection-using-cnn/

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🖥 Machine learning tutorials (mainly in Python3)

Machine learning tutorials. A collection of 100+ machine learning lessons written primarily in python.

▪Github: https://github.com/ethen8181/machine-learning

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Deep Learning
NLP
AI
Python
ML
Data Mining
Tensorflow
Keras

👇👇👇👇👇
@Machine_learn

Artificial Intelligence && Deep Learning
Channel for who have a passion for -

* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers

https://t.iss.one/DeepLearning_ai

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190+ Python Interview Questions and Answers (2024)

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