Machine Learning with Python

🔥 Trending Repository: awesome-deeplearning-resources

📝 Description: Deep Learning and deep reinforcement learning research papers and some codes

🔗 Repository URL: https://github.com/endymecy/awesome-deeplearning-resources

📖 Readme: https://github.com/endymecy/awesome-deeplearning-resources#readme

📊 Statistics:
🌟 Stars: 2.9K stars
👀 Watchers: 221
🍴 Forks: 666 forks

💻 Programming Languages: Not available

🏷️ Related Topics:

#nlp #video #reinforcement_learning #deep_learning #neural_network #code #paper #corpus #modelzoo

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🧠 By: https://t.me/DataScienceN

🔥 Trending Repository: awesome-transformer-nlp

📝 Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.

🔗 Repository URL: https://github.com/cedrickchee/awesome-transformer-nlp

📖 Readme: https://github.com/cedrickchee/awesome-transformer-nlp#readme

📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 41
🍴 Forks: 131 forks

💻 Programming Languages: Not available

🏷️ Related Topics:

#nlp #natural_language_processing #awesome #transformer #neural_networks #awesome_list #llama #transfer_learning #language_model #attention_mechanism #bert #gpt_2 #xlnet #pre_trained_language_models #gpt_3 #gpt_4 #chatgpt

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🧠 By: https://t.me/DataScienceM

Python library that adds Generative AI capabilities to Pandas!

PandasAI analyzes complex data frames and plot visualizations just by using natural language.

GitHub: https://github.com/sinaptik-ai/pandas-ai

https://t.me/CodeProgrammer 🖕

✅ @Machine_learn

Data scientists, this is for you — I dug up LeetCode for DS

DataLemur — a powerful platform that collects real interview problems from Tesla, Facebook, Twitter, Microsoft, and other top companies

Inside: practical tasks on SQL, statistics, Python, and ML. You can filter by difficulty level and company

Top-notch for those preparing for interviews for Data Scientist / Data Analyst roles. Get it here 🍯

👉 https://t.me/CodeProgrammer 👍

python-docx: Create and Modify Word Documents #python

python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.

Installation

pip install python-docx

Example

from docx import Document

document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")

document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'

https://t.me/DataScienceN 🚗

Resources:
https://t.me/+r_Tcx2c-oVU1OWNi

Top 150 Python Interview Questions

This PDF covers the most frequently asked Python questions with answers to help you prepare for upcoming interviews.

🔸Link to PDF

👉 @DataScience4

🚀 16th AI by Hand ✍️ Workshops, Scholarships available 👉 https://lu.ma/te2q2zqu

Every Wednesday we’ve been bringing people together to learn AI by hand ✍️.

Next week marks our 16th workshop, and thanks to Google’s generous sponsorship, we’re offering scholarships for educators and students to join us.

Choose your session:

🙌 Deep Learning Beginner Math Workshop
Build the math foundation for deep learning:

1. Dot Product
2. Matrix Multiplication
3. Linear Layer
4. Activation
5. Artificial Neuron

🙌 🙌 Transformer in Excel Workshop (Intermediate)
For AI engineers who know how to use Transformers but want to open the black box. We’ll visualize every step—data flow, math, and dimension alignment—inside Excel.

🙌 🙌 🙌 Latest AI Paper Workshop (Advanced)
Work through a just-published model, architecture, or algorithm with brand-new AI by Hand exercises—crafted for this workshop only.

🙌 🙌 🙌 Deep Reinforcement Learning Workshop (Advanced)
From replay buffers to Monte Carlo, TD learning, Deep Q-Networks, and SARSA—understand value-based deep RL from the ground up.

📅 When: Every Wednesday
🎓 Scholarships: Available for educators & students (sponsored by Google)

Register 🔗 https://lu.ma/te2q2zqu

Grab these free AI courses before they get paywalled:

𝟭. Prompt Engineering Basics:
https://skillbuilder.aws/search?searchText=foundations-of-prompt-engineering&showRedirectNotFoundBanner=true

𝟮. ChatGPT Prompts Mastery:
https://deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

𝟯. Intro to Generative AI:
https://cloudskillsboost.google/course_templates/536

𝟰. AI Introduction by Harvard:
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05

𝟱. Microsoft GenAI Basics:
https://linkedin.com/learning/what-is-generative-ai/generative-ai-is-a-tool-in-service-of-humanity

𝟲. Prompt Engineering Pro:
https://learnprompting.org

𝟳. Google’s Ethical AI:
https://cloudskillsboost.google/course_templates/554

𝟴. Harvard Machine Learning:
https://pll.harvard.edu/course/data-science-machine-learning

𝟵. LangChain App Developer:
https://deeplearning.ai/short-courses/langchain-for-llm-application-development/

𝟭𝟬. Bing Chat Applications:
https://linkedin.com/learning/streamlining-your-work-with-microsoft-bing-chat

𝟭𝟭. Generative AI by Microsoft:
https://learn.microsoft.com/en-us/training/paths/introduction-to-ai-on-azure/

𝟭𝟮. Amazon’s AI Strategy:
https://skillbuilder.aws/search?searchText=generative-ai-learning-plan-for-decision-makers&showRedirectNotFoundBanner=true

𝟭𝟯. GenAI for Everyone:
https://deeplearning.ai/courses/generative-ai-for-everyone/

𝟭𝟰. AWS GenAI Foundation:
https://coursera.org/learn/generative-ai-with-llms

https://t.me/CodeProgrammer 🔰

#LSTMs made AI remember before #Transformers took over

here’s the 15-step by-hand ✍️ guide

you can download: https://www.byhand.ai/p/26-lstm

https://t.me/CodeProgrammer

Microsoft launched the best course on Generative AI!

The Free 21 lesson course is available on #Github and will teach you everything you need to know to start building #GenerativeAI applications.

Enroll: https://github.com/microsoft/generative-ai-for-beginners

https://github.com/microsoft/generative-ai-for-beginners

https://t.me/CodeProgrammer 🩷

LangExtract

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.

GitHub: https://github.com/google/langextract

https://t.me/DataScienceN 🖕

🚀 Model Context Protocol (MCP) Curriculum for Beginners

Learn MCP with Hands-on Code Examples in C#, Java, JavaScript, Python, and TypeScript
🧠 Overview of the Model Context Protocol Curriculum

The Model Context Protocol (MCP) is an innovative framework designed to standardize communication between AI models and client applications. This open-source curriculum provides a structured learning path, featuring practical coding examples and real-world scenarios across popular programming languages such as C#, Java, JavaScript, TypeScript, and Python.

Whether you're an AI developer, system architect, or software engineer, this guide is your all-in-one resource for mastering MCP fundamentals and implementation techniques.

Resources: https://github.com/microsoft/mcp-for-beginners/blob/main/translations/en/README.md

https://t.me/CodeProgrammer ⭐️

Deep Learning roadmap. Now it’s your turn!

𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗪𝗲𝗲𝗸 𝟭-𝟮)
● Understand perceptrons, sigmoid, ReLU, tanh
● Learn cost functions, gradient descent, and derivatives
● Implement binary logistic regression using NumPy

𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗦𝗵𝗮𝗹𝗹𝗼𝘄 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟯-𝟰)
● Build a neural net with one hidden layer
● Compare activation functions (sigmoid vs tanh vs ReLU)
● Train your model to classify simple images

𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗲𝗽 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗪𝗲𝗲𝗸 𝟱-𝟲)
● Forward and backward propagation through multiple layers
● Parameter initialization and tuning
● Implement L-layer neural networks from scratch

𝗣𝗵𝗮𝘀𝗲 𝟰: 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗪𝗲𝗲𝗸 𝟳-𝟴)
● Learn mini-batch gradient descent, RMSProp, and Adam
● Apply L2 and Dropout regularization to avoid overfitting
● Boost your model’s performance with better convergence

𝗣𝗵𝗮𝘀𝗲 𝟱: 𝗧𝗲𝗻𝘀𝗼𝗿𝗙𝗹𝗼𝘄 & 𝗥𝗲𝗮𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗪𝗲𝗲𝗸 𝟵-𝟭𝟬)
● Build models using TensorFlow and Keras
● Normalize data, tune hyperparameters, and visualize metrics
● Create multi-class classifiers using softmax

𝗣𝗵𝗮𝘀𝗲 𝟲: 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗖𝗮𝗿𝗲𝗲𝗿 𝗣𝗿𝗲𝗽 (𝗪𝗲𝗲𝗸 𝟭𝟭-𝟭𝟮)
● Work on image recognition, text classification, and real datasets
● Learn model deployment techniques
● Prepare for interviews with hands-on projects and GitHub repo

https://t.me/CodeProgrammer ✉️

“Learn AI” is everywhere. But where do the builders actually start?
Here’s the real path, the courses, papers and repos that matter.

✅ Videos:

Everything here ⇒ https://lnkd.in/ePfB8_rk

➡️ LLM Introduction → https://lnkd.in/ernZFpvB
➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn
➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq
➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW
➡️ Building Effective Agents → https://lnkd.in/eqxvBg79
➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K
➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn

✅ Courses:

All Courses here ⇒ https://lnkd.in/eKKs9ves

➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE
➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP
➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs
➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs
➡️ Agent Memory → https://lnkd.in/egC8h9_Z
➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa
➡️ Building Browser Agents → https://lnkd.in/ewy3sApa
➡️ LLMOps → https://lnkd.in/ex4xnE8t
➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW
➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU
➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR
➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M
➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9
➡️ Multi Agent Systems → https://lnkd.in/evBnavk9

✅ Guides:

Access all ⇒ https://lnkd.in/e-GA-HRh

➡️ Google's Agent → https://lnkd.in/encAzwKf
➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg
➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ
➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju
➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh

✅ Repos:
➡️ GenAI Agents → https://lnkd.in/eAscvs_i
➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic
➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP
➡️ AI Agent Papers → https://lnkd.in/esMHrxJX

✅ Papers:
🟡 ReAct → https://lnkd.in/eZ-Z-WFb
🟡 Generative Agents → https://lnkd.in/eDAeSEAq
🟡 Toolformer → https://lnkd.in/e_Vcz5K9
🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq
🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S
🟡 Reflexion → https://lnkd.in/eggND2rZ
🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE

Access all ⇒ https://lnkd.in/e-GA-HRh

By: https://t.me/CodeProgrammer 🟡

Top 140 PyTorch Interview Questions and Answers

This comprehensive guide covers essential PyTorch interview questions across multiple categories, with detailed explanations for each.these 140 carefully curated questions represent the most important concepts you'll encounter in #PyTorch interviews.

🧠 Link: https://hackmd.io/@husseinsheikho/pytorch-interview

https://t.me/CodeProgrammer

800+ Data Science Interview Questions – A Must-Have Resource for Every Aspirant

Breaking into the data science field is challenging—not because of a lack of opportunities, but because of how thoroughly you need to prepare.

This document, curated by Steve Nouri, is a goldmine of 800+ real-world interview questions covering:

-Statistics
-Data Science Fundamentals
-Data Analysis
-Machine Learning
-Deep Learning
-Python & R
-Model Evaluation & Optimization
-Deployment Strategies
…and much more!

https://t.me/CodeProgrammer 🔰