🖥 Fake news Detection Machine Learning Project with 92%Accuracy

✅ it contain compressed file in which "jupyter notebook file and dataset" ✅

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🤖 dear data scientists; You don't need to write code in Jupyter notebook anymore! With the Anaconda Assistant added to Jupiter Notebook, you can write a prompt and the code you want will be generated automatically!

Anaconda Assistant brings productive artificial intelligence to Python and data analysis and makes working with data easy for anyone. You can access this feature for free in your Anaconda notebook! 💯

💬 Among the uses of Anaconda Assistant:

🚀 Generate code and improve the coding experience
📊 Data visualization and analysis with more efficiency
📈 drawing diagrams
🐱 Explanation of the data frame and comments

🔖 Anaconda Assistant training guide:

┌ 🏷 Anaconda Assistant
├ 🚀 Anaconda Assistant
└ 🔖 Getting started

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🖥 The most comprehensive educational resource for learning NLP

✅ If you want to have the most comprehensive educational resource for learning NLP , The Index NLP Don't miss it in any way! This site is recommended as a comprehensive and vital resource for people interested in NLP research, which includes more than 9000 related articles and codes , helping you to easily find the codes and articles you need to understand and implement your new research. .

This wonderful resource was launched by Quantum Stat and includes information such as article titles, abstracts, authors, and links to related articles and code repositories. You also have the possibility of quick search and connection diagrams between similar articles


┌ 🏷 The NLP Index
└ 💣 The NLP Index

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🦾 Made With ML : Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications.

A 100% free course that will help you learn how to write production-grade MLOps code.

The course will teach you everything from design, modeling, testing, working with learning models and much more for free!

More than 35 thousand stars on Github

Learn how to design, develop, deploy, and operate production-grade ML applications.

▪ Course
▪ Overview
▪ Jupyter notebook

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☄️ Top 12 YouTube Channels to Learn Python

💐 Python will include 57% of data scientist job ads in 2024 . It is still the number one programming language for data scientists.

✅ Now, if you are looking for the best resources to improve your Python skills, after searching and reviewing various resources, I have prepared a list of 12 top channels that provide first-class Python training, which can turn beginners into professional Python programmers. convert


🎬 Python Programmer channel
┤ 📈 211 videos / 465K SUB
┘ 🔴 Link: Python Programmer


🎬 Luke Barousse channel
┤ 📈 157 videos / 429K SUB
┘ 🔴 Link: Luke Barousse


🎬 codebasics channel
┤ 📈 837 videos / 990K SUB
┘ 🔴 link: codebasics


🎬 StatQuest channel with Josh Starmer
┤ 📈 271 videos / 1.14M SUB
┘ 🔴 Link: StatQuest with Josh Starmer


🎬 Sundas Khalid channel
┤ 📈 143 videos / 203K SUB
┘ 🔴 Link: Sundas Khalid


🎬 Shashank Kalanithi channel
┤ 📈 152 videos / 148K SUB
┘ 🔴 Link: Shashank Kalanithi


🎬 Programming with Mosh channel
┤ 📈 203 videos / 3.85M SUB
┘ 🔴 Link: Programming with Mosh


🎬 Corey Schafer channel
┤ 📈 233 videos / 129K SUB
┘ 🔴 Link: Corey Schafer


🎬 sentdex channel
┤ 📈 1254 videos / 1.3M SUB
┘ 🔴 link: sentdex


🎬 Patrick Loeber channel
┤ 📈 206 videos / 264K SUB
┘ 🔴 Link: Patrick Loeber


🎬 Socratica channel
┤ 📈 659 videos / 876K SUB
┘ 🔴 Link: Socratica


🎬 Tech With Tim channel
┤ 📈 983 videos / 1.48M SUB
┘ 🔴 Link: Tech With Tim

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🔥 Large selection of interview questions on DS, AI, ML, DL, NLP, computer vision.

✅ Machine learning interview questions and answers for data scientists

✅ Deep Learning Interview Questions and Answers for Data Scientists

✅ Statistics questions

✅ Questions on probability theory

✅ Repository for a line of online courses on statistics

✅ Questions and answers for data scientists in Python

✅ SQL and DB Interview Questions and Answers for Data Scientists
Questions Based on Resumes

✅ Large Language Model (LLM) Interview Questions and Answers

✅ Questions and answers to interviews on computer vision part 1

✅ Computer vision interview questions and answers part 2

✅ Questions and answers to computer vision interviews part 3

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✅ Good evening: We have launched an urgent donation campaign in order to continue our channels with the momentum you are accustomed to. Contribute if you think our work deserves thanks.

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✅ Learn PyTorch in 4 steps

👨🏻‍💻 With support for more than 200 different mathematical operations, PyTorch is one of the most powerful open source and computational libraries based on Python for machine learning. You don't need to look for different sources to learn this popular library, but the website of this library covers more than what you need.💯

2️⃣ Here, I will teach you how to learn PyTorch from scratch in 4 steps. 👌🏼


1️⃣ Text tutorial: If you don't know anything about Python, you should start learning from here.
⬅️ Topics: tensors, datasets and DataLoader, model building, optimization loop, saving, loading and using the model.

📄 PyTorch Basics
┘📎 Link: Learn the Basics


2️⃣ Video training: This section is very useful for those who prefer video content.
⬅️ Topics: introduction, tensor construction and model construction with pytorch.

📄 Introduction to PyTorch
┘📎 Link: Introduction to PyTorch


3️⃣ Examining examples: By viewing different examples, you will review what you have learned so far.

📄 Learning Python by example
┘📎 Link: Learning PyTorch with Examples


4️⃣ Coding: This step is vital! Your theoretical knowledge is worthless without coding! The Keras website is full of sample code that is great for getting started.

📄 Start coding
┘📎 Link: keras.io

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Some Helpful Data Science Projects for Beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

Intermediate Level Data science Projects

Black Friday Data : https://www.kaggle.com/sdolezel/black-friday

Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones

Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset

Million Song Data : https://www.kaggle.com/c/msdchallenge

Census Income Data : https://www.kaggle.com/c/census-income/data

Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset

Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2

Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining

➖➖➖➖➖➖➖➖➖➖
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➖➖➖➖➖➖➖➖➖➖

⚡️ Graph Machine Learning

Free advanced course: Machine learning on graphs .

The course is regularly supplemented with practical problems and slides. The author Xavier Bresson is a professor at the National University of Singapore.

▪ Introduction

▪ Dive into graphs
- Lab1: Generate LFR social networks
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code01.ipynb

- Lab2: Visualize spectrum of point cloud & grid
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code02.ipynb

- Lab3/4: Graph construction for two-moon & text documents
https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code03.ipynb

https://github.com/xbresson/GML2023/blob/main/codes/02_Graph_Science/code04.ipynb

▪ Graph clustering
- Lab1: k-means
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code01.ipynb

https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code02.ipynb

- Lab2: Metis
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code03.ipynb

- Lab3/4: NCut/PCut
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code04.ipynb

https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code05.ipynb

- Lab5: Louvain
https://github.com/xbresson/GML2023/blob/main/codes/03_Graph_Clustering/code06.ipynb
https://pic.twitter.com/vSXCx364pe

▪ Lectures 4 Graph SVM
- Lab1 : Standard/Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code01.ipynb

- Lab2 : Soft-Margin SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code02.ipynb

- Lab3 : Kernel/Non-Linear SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code03.ipynb

- Lab4 : Graph SVM
https://github.com/xbresson/GML2023/blob/main/codes/04_Graph_SVM/code04.ipynb

Running instructions: https://storage.googleapis.com/xavierbresson/lectures/CS6208/running_notebooks.pdf

💡 Github

✅ https://hottg.com/DataScienceT

🔺 The best GitHub repositories for learning Python
✅ Learn Python for Data Science in 2024

👨🏻‍💻 In the latest data science report 2024 , Python is still the top programming language for data science with 56.7% . Here I have put a list of the best Python repositories for data science , which will improve your coding skills and guide you on the path to data science mastery.💯

1️⃣ Learn Python 3 repo
🖥 A collection of Jupyter notebooks for learning Python.
🐱 GitHub repo link

2️⃣ The Algorithms repo
🖥 All algorithms implemented in Python for training.
🐱 GitHub repo link

3️⃣ Awesome Python repo
🖥 A list of great Python frameworks, libraries, software, and resources.
🐱 GitHub repo link

4️⃣ 100 Days of ML repo
🖥 Learning algorithms and building neural networks without any programming experience.
🐱 GitHub repo link

5️⃣ Cosmic Python book repo
🖥 A book on Python's functional architectural patterns for managing complexity.
🐱 GitHub repo link

6️⃣ A Byte of Python book repo
🖥 If you do not learn Python programming, start with this book.
🐱 GitHub repo link

7️⃣ Python Machine Learning book repo
🖥 Python Machine Learning book code repository.
🐱 GitHub repo link

8️⃣ Repo of interactive interview challenges
🖥 120+ interactive Python coding interview challenges.
🐱 GitHub repo link

9️⃣ Repo of coding problems
🖥 Solutions for various coding/algorithmic problems.
🐱 GitHub repo link

1️⃣ Python Basics repo
🖥 A list of 300 Python interview questions + answer sheet.
🐱 GitHub repo link

1️⃣ Python programming exercises repo
🖥 100+ challenging Python programming exercises.
🐱 GitHub repo link
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In 1989, Yann LeCun and his team trained a LeNet 1 CNN, which was able to detect handwriting.

They published a video showing how this model can read the numbers that were written manually on a piece of paper, and then the model gives the numbers electronically.

The Convolution Neural Network CNN algorithm is considered one of the algorithms that has influenced the world and we find it nowadays in many fields.

In general, everything that can be predicted from an image or video is a CNN.
Many researchers relied on this algorithm and derived many of the most famous models from it (ResNet, DenseNet, MobileNet, SqueezeNet, VGG)

There are many models that come under the name CNN
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🔥🪄 Awesome-LLM : a curated list of Large Language Model

A curated list of articles, models, api, code examples, courses, datasets dedicated to large language models.

This is a well- structured academic selection.

• Github

Other highly specialized awesome repositories dedicated to LLM:
• Awesome-LLM-hallucination
• Awesome-hallucination-detection
• Awesome ChatGPT Prompts
• Awesome ChatGPT
• Awesome Deliberative Prompting
• Instruction-Tuning-Papers
• LLM Reading List
• Reasoning using Language Models
• Chain-of-Thought Hub
• Awesome GPT
• Awesome GPT-3
• Awesome LLM Human Preference Datasets
• RWKV-howto
• ModelEditingPapers
• Awesome LLM Security
• Awesome-Code-LLM
• Awesome-LLM-Compression
• Awesome-LLM-Systems
• Awesome-LLM-Healthcare
• Awesome-LLM-Inference
• Awesome-LLM-3D
• LLMDatahub
• Language models for Russian language

〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️
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🟢 Yaoliang Yu, a professor at the School of Computer Science at the University of Waterloo, Canada, has published several free data science courses. These courses include machine learning, data science optimization, linear algebra and deep learning.

✅ The resources of each course include textbooks, assignments, articles and projects during the course.

🔖 Guide to free data science courses at the University of Waterloo:


┌ ➡️ CS794 Fall 2022
└ 🖥 Optimization for Data Science

┌ ➡️ CS480 Fall 2022
└ 🖥 Introduction to Machine Learning

┌ ➡️ CS794 Fall 2021
└ 🖥 Game Theoretic Methods in ML

┌ ➡️ CS480 Fall 2019
└ 🧠 Theory of Deep Learning

┌ ➡️ CS475 Spring 2018
└ 🖥 Computational Linear Algebra

〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️
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