Python | Machine Learning | Coding | R
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Discover powerful insights with Python, Machine Learning, Coding, and R—your essential toolkit for data-driven solutions, smart alg

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10 GitHub repos to build a career in AI engineering:

(100% free step-by-step roadmap)

1️⃣ ML for Beginners by Microsoft

A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.

Includes quizzes, lessons, and hands-on projects, with some videos.

GitHub repo → https://lnkd.in/dCxStbYv

2️⃣ AI for Beginners by Microsoft

This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.

Beginner-friendly, project-based, and full of real-world apps.

GitHub repo → https://lnkd.in/dwS5Jk9E

3️⃣ Neural Networks: Zero to Hero

Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.

This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.

GitHub repo → https://lnkd.in/dXAQWucq

4️⃣ DL Paper Implementations

So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.

This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.

GitHub repo → https://lnkd.in/dTrtDrvs

5️⃣ Made With ML

Now it’s time to learn how to go from notebooks to production.

Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.

GitHub repo → https://lnkd.in/dYyjjBGb

6️⃣ Hands-on LLMs

- You've built neural nets.
- You've explored GPTs and LLMs.

Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.

GitHub repo → https://lnkd.in/dh2FwYFe

7️⃣ Advanced RAG Techniques

Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.

This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.

GitHub repo → https://lnkd.in/dBKxtX-D

8️⃣ AI Agents for Beginners by Microsoft

After diving into LLMs and mastering RAG, learn how to build AI agents.

This hands-on course covers building AI agents using frameworks like AutoGen.

GitHub repo → https://lnkd.in/dbFeuznE

9️⃣ Agents Towards Production

The above course will teach what AI agents are. Next, learn how to ship them.

This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.

GitHub repo → https://lnkd.in/dcwmamSb

🔟 AI Engg. Hub

To truly master LLMs, RAG, and AI agents, you need projects.

This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.

GitHub repo → https://lnkd.in/geMYm3b6

#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers


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mcp guide.pdf.pdf
16.7 MB
A comprehensive PDF has been compiled that includes all MCP-related posts shared over the past six months.

(75 pages, 10+ projects & visual explainers)

Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:

* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers

Projects included:

1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit

#MCP #ModularComputationProtocol #AIProjects #DeepLearning #ArtificialIntelligence #RAG #VoiceAI #SyntheticData #AIAgents #AIResearch #TechWriting #OpenSourceAI #AI #python

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Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10

It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.

= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)

#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers


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GPU by hand ✍️ I drew this to show how a GPU speeds up an array operation of 8 elements in parallel over 4 threads in 2 clock cycles. Read more 👇

CPU
• It has one core.
• Its global memory has 120 locations (0-119).
• To use the GPU, it needs to copy data from the global memory to the GPU.
• After GPU is done, it will copy the results back.

GPU
• It has four cores to run four threads (0-3).
• It has a register file of 28 locations (0-27)
• This register file has four banks (0-3).
• All threads share the same register file.
• But they must read/write using the four banks.
• Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.

#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers


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What is torch.nn really?

When I started working with PyTorch, my biggest question was: "What is torch.nn?".


This article explains it quite well.

📌 Read

#pytorch #AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers


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NUMPY FOR DS.pdf
4.5 MB
Let's start at the top...

NumPy contains a broad array of functionality for fast numerical & mathematical operations in Python

The core data-structure within #NumPy is an ndArray (or n-dimensional array)

Behind the scenes - much of the NumPy functionality is written in the programming language C

NumPy functionality is used in other popular #Python packages including #Pandas, #Matplotlib, & #scikitlearn!

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Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts

---

1. What is a Dataset?

• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.

---

2. Types of Datasets

Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).

Unstructured Data: Images, text, audio, video.

Semi-structured Data: JSON, XML files containing hierarchical data.

---

3. Common Dataset Formats

• CSV (Comma-Separated Values)

• Excel (.xls, .xlsx)

• JSON (JavaScript Object Notation)

• XML (eXtensible Markup Language)

• Images (JPEG, PNG, TIFF)

• Audio (WAV, MP3)

---

4. Loading Datasets in Python

• Use libraries like pandas for structured data:

import pandas as pd
df = pd.read_csv('data.csv')


• Use libraries like json for JSON files:

import json
with open('data.json') as f:
data = json.load(f)


---

5. Basic Dataset Exploration

• Check shape and size:

print(df.shape)


• Preview data:

print(df.head())


• Check for missing values:

print(df.isnull().sum())


---

6. Summary

• Understanding dataset types is crucial before processing.

• Loading and exploring datasets helps identify cleaning and preprocessing needs.

---

Exercise

• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.

---

#DataScience #Datasets #DataLoading #Python #DataExploration

The rest of the parts 👇
https://t.me/DataScienceM 🌟
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Topic: Python Script to Convert a Shared ChatGPT Link to PDF – Step-by-Step Guide

---

### Objective

In this lesson, we’ll build a Python script that:

• Takes a ChatGPT share link (e.g., https://chat.openai.com/share/abc123)
• Downloads the HTML content of the chat
• Converts it to a PDF file using pdfkit and wkhtmltopdf

This is useful for archiving, sharing, or printing ChatGPT conversations in a clean format.

---

### 1. Prerequisites

Before starting, you need the following libraries and tools:

#### • Install pdfkit and requests

pip install pdfkit requests


#### • Install wkhtmltopdf

Download from:
https://wkhtmltopdf.org/downloads.html

Make sure to add the path of the installed binary to your system PATH.

---

### 2. Python Script: Convert Shared ChatGPT URL to PDF

import pdfkit
import requests
import os

# Define output filename
output_file = "chatgpt_conversation.pdf"

# ChatGPT shared URL (user input)
chat_url = input("Enter the ChatGPT share URL: ").strip()

# Verify the URL format
if not chat_url.startswith("https://chat.openai.com/share/"):
print("Invalid URL. Must start with https://chat.openai.com/share/")
exit()

try:
# Download HTML content
response = requests.get(chat_url)
if response.status_code != 200:
raise Exception(f"Failed to load the chat: {response.status_code}")

html_content = response.text

# Save HTML to temporary file
with open("temp_chat.html", "w", encoding="utf-8") as f:
f.write(html_content)

# Convert HTML to PDF
pdfkit.from_file("temp_chat.html", output_file)

print(f"\n PDF saved as: {output_file}")

# Optional: remove temp file
os.remove("temp_chat.html")

except Exception as e:
print(f" Error: {e}")


---

### 3. Notes

• This approach works only if the shared page is publicly accessible (which ChatGPT share links are).
• The PDF output will contain the web page version, including theme and layout.
• You can customize the PDF output using pdfkit options (like page size, margins, etc.).

---

### 4. Optional Enhancements

• Add GUI with Tkinter
• Accept multiple URLs
• Add PDF metadata (title, author, etc.)
• Add support for offline rendering using BeautifulSoup to clean content

---

### Exercise

• Try converting multiple ChatGPT share links to PDF
• Customize the styling with your own CSS
• Add a timestamp or watermark to the PDF

---

#Python #ChatGPT #PDF #WebScraping #Automation #pdfkit #tkinter

https://t.me/CodeProgrammer
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Python | Machine Learning | Coding | R
Photo
# 📚 Python Tutorial: Convert EPUB to PDF (Preserving Images)
#Python #EPUB #PDF #EbookConversion #Automation

This comprehensive guide will show you how to convert EPUB files (including those with images) to high-quality PDFs using Python.

---

## 🔹 Required Tools & Libraries
We'll use these Python packages:
- ebooklib - For EPUB parsing
- pdfkit (wrapper for wkhtmltopdf) - For PDF generation
- Pillow - For image handling (optional)

pip install ebooklib pdfkit pillow


Also install system dependencies:
# On Ubuntu/Debian
sudo apt-get install wkhtmltopdf

# On MacOS
brew install wkhtmltopdf

# On Windows (download from wkhtmltopdf.org)


---

## 🔹 Step 1: Extract EPUB Contents
First, we'll unpack the EPUB file to access its HTML and images.

from ebooklib import epub
from bs4 import BeautifulSoup
import os

def extract_epub(epub_path, output_dir):
book = epub.read_epub(epub_path)

# Create output directory
os.makedirs(output_dir, exist_ok=True)

# Extract all items (chapters, images, styles)
for item in book.get_items():
if item.get_type() == epub.ITEM_IMAGE:
# Save images
with open(os.path.join(output_dir, item.get_name()), 'wb') as f:
f.write(item.get_content())
elif item.get_type() == epub.ITEM_DOCUMENT:
# Save HTML chapters
with open(os.path.join(output_dir, item.get_name()), 'wb') as f:
f.write(item.get_content())

return [item.get_name() for item in book.get_items() if item.get_type() == epub.ITEM_DOCUMENT]


---

## 🔹 Step 2: Convert HTML to PDF
Now we'll convert the extracted HTML files to PDF while preserving images.

import pdfkit
from PIL import Image # For image validation (optional)

def html_to_pdf(html_files, output_pdf, base_dir):
options = {
'encoding': "UTF-8",
'quiet': '',
'enable-local-file-access': '', # Critical for local images
'no-outline': None,
'margin-top': '15mm',
'margin-right': '15mm',
'margin-bottom': '15mm',
'margin-left': '15mm',
}

# Validate images (optional)
for html_file in html_files:
soup = BeautifulSoup(open(os.path.join(base_dir, html_file)), 'html.parser')
for img in soup.find_all('img'):
img_path = os.path.join(base_dir, img['src'])
try:
Image.open(img_path) # Validate image
except Exception as e:
print(f"Image error in {html_file}: {e}")
img.decompose() # Remove broken images

# Convert to PDF
pdfkit.from_file(
[os.path.join(base_dir, f) for f in html_files],
output_pdf,
options=options
)


---

## 🔹 Step 3: Complete Conversion Function
Combine everything into a single workflow.

def epub_to_pdf(epub_path, output_pdf, temp_dir="temp_epub"):
try:
print(f"Converting {epub_path} to PDF...")

# Step 1: Extract EPUB
print("Extracting EPUB contents...")
html_files = extract_epub(epub_path, temp_dir)

# Step 2: Convert to PDF
print("Generating PDF...")
html_to_pdf(html_files, output_pdf, temp_dir)

print(f"Success! PDF saved to {output_pdf}")
return True

except Exception as e:
print(f"Conversion failed: {str(e)}")
return False
finally:
# Clean up temporary files
if os.path.exists(temp_dir):
import shutil
shutil.rmtree(temp_dir)


---

## 🔹 Advanced Options
### 1. Custom Styling
Add CSS to improve PDF appearance:

def html_to_pdf(html_files, output_pdf, base_dir):
options = {
# ... previous options ...
'user-style-sheet': 'styles.css', # Custom CSS
}

# Create CSS file if needed
css = """
body { font-family: "Times New Roman", serif; font-size: 12pt; }
img { max-width: 100%; height: auto; }
"""
with open(os.path.join(base_dir, 'styles.css'), 'w') as f:
f.write(css)

pdfkit.from_file(/* ... */)
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📚 JaidedAI/EasyOCR — an open-source Python library for Optical Character Recognition (OCR) that's easy to use and supports over 80 languages out of the box.

### 🔍 Key Features:

🔸 Extracts text from images and scanned documents — including handwritten notes and unusual fonts
🔸 Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
🔸 Built on PyTorch — uses modern deep learning models (not the old-school Tesseract)
🔸 Simple to integrate into your Python projects

### Example Usage:

import easyocr

reader = easyocr.Reader(['en', 'ru']) # Choose supported languages
result = reader.readtext('image.png')


### 📌 Ideal For:

Text extraction from photos, scans, and documents
Embedding OCR capabilities in apps (e.g. automated data entry)

🔗 GitHub: https://github.com/JaidedAI/EasyOCR

👉 Follow us for more: @DataScienceN

#Python #OCR #MachineLearning #ComputerVision #EasyOCR
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🧹 ObjectClear — an AI-powered tool for removing objects from images effortlessly.

⚙️ What It Can Do:

🖼️ Upload any image
🎯 Select the object you want to remove
🌟 The model automatically erases the object and intelligently reconstructs the background

⚡️ Under the Hood:

— Uses Segment Anything (SAM) by Meta for object segmentation
— Leverages Inpaint-Anything for realistic background generation
— Works in your browser with an intuitive Gradio UI

✔️ Fully open-source and can be run locally.

📎 GitHub: https://github.com/zjx0101/ObjectClear

#AI #ImageEditing #ComputerVision #Gradio #OpenSource #Python


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🚀 Comprehensive Tutorial: Build a Folder Monitoring & Intruder Detection System in Python

In this comprehensive, step-by-step tutorial, you will learn how to build a real-time folder monitoring and intruder detection system using Python.

🔐 Your Goal:
Create a background program that:
- Monitors a specific folder on your computer.
- Instantly captures a photo using the webcam whenever someone opens that folder.
- Saves the photo with a timestamp in a secure folder.
- Runs automatically when Windows starts.
- Keeps running until you manually stop it (e.g., via Task Manager or a hotkey).

Read and get code: https://hackmd.io/@husseinsheikho/Build-a-Folder-Monitoring

#Python #Security #FolderMonitoring #IntruderDetection #OpenCV #FaceCapture #Automation #Windows #TaskScheduler #ComputerVision


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