Python is a high-level, interpreted scripting language, highly used for automation and backend purposes.
People from different disciplines use Python for a variety of different tasks, such as data analysis and visualization, artificial intelligence and Machine Learning,
automation, etc.
It is a language that many people fall in love with because of its simplicity. The syntax is simple and the debugging is also much easier in Python compared to languages like C++ or C# even though Python was made using C++ in 1991 by Guido van Rossum.
Index
Python Playlists on Youtube
Python for Different Fields 🔗
Other Useful GitHub Repositories
AI & Machine Learning with Python 🤖
Generative AI & Large Language Models
| Resource Name |
Link |
Difficulty |
| PyTorch Official Tutorials |
Click Here |
Intermediate |
| TensorFlow & Keras Guide |
Click Here |
Intermediate |
| JAX Documentation |
Click Here |
Advanced |
| Scikit-learn User Guide |
Click Here |
Beginner |
| XGBoost Documentation |
Click Here |
Intermediate |
| Resource Name |
Link |
Difficulty |
| Andrej Karpathy - Neural Networks: Zero to Hero |
Click Here |
Intermediate |
| 3Blue1Brown - Neural Networks |
Click Here |
Beginner |
| Fast.ai Practical Deep Learning |
Click Here |
Intermediate |
| Sentdex - ML with Python |
Click Here |
Beginner |
| DeepLearning.AI Short Courses |
Click Here |
Beginner |
| Resource Name |
Link |
Difficulty |
| Hands-On Machine Learning (3rd Ed.) |
Click Here |
Intermediate |
| Deep Learning with Python - Chollet |
Click Here |
Intermediate |
| NLP with Transformers |
Click Here |
Intermediate |
| OpenAI Cookbook |
Click Here |
Beginner |
| Hugging Face Course (Free) |
Click Here |
Beginner |
AI/ML Blogs & Communities
AI Coding Agents for Python Development 🧑💻
| Resource Name |
Link |
Notes |
| Claude Code |
Click Here |
Terminal agent, strong at Python codebase understanding & refactors |
| Cursor |
Click Here |
AI-first VS Code fork, great Python autocomplete + Composer |
| GitHub Copilot |
Click Here |
Python-aware autocomplete & agent mode in most IDEs |
| OpenAI Codex CLI |
Click Here |
Terminal coding agent, solid for Python scripting tasks |
| Cline |
Click Here |
Open-source VS Code agent, writes & runs Python directly |
| Aider |
Click Here |
Git-aware pair programming in the terminal, Python-first UX |
Building AI Agents in Python 🤖
| Resource Name |
Link |
Notes |
| LangGraph |
Click Here |
Python framework for stateful, controllable multi-agent apps |
| CrewAI |
Click Here |
Pure-Python role-based multi-agent framework |
| AG2 (formerly AutoGen) |
Click Here |
Python framework for conversational multi-agent systems |
| Semantic Kernel (Python) |
Click Here |
Microsoft's agent SDK, first-class Python support |
| OpenAI Agents SDK |
Click Here |
Lightweight Python agent + handoff primitives |
| Smolagents (Hugging Face) |
Click Here |
~1000-line minimal Python agent loop, easy to learn from |
| LlamaIndex Workflows |
Click Here |
Python event-driven agent orchestration |
| Pydantic AI |
Click Here |
Type-safe Python agent framework from the Pydantic team |
Model Context Protocol (MCP) with Python 🔌
| Resource Name |
Link |
Notes |
| MCP Official Docs & Spec |
Click Here |
Protocol spec + Python SDK guide |
| Python MCP SDK |
Click Here |
Official Python SDK for building MCP servers/clients |
| MCP Reference Servers |
Click Here |
Official reference servers, many written in Python |
| Awesome MCP Servers |
Click Here |
Curated directory of MCP servers (filter by Python) |
Evaluating & Testing LLM Apps in Python 📊
| Resource Name |
Link |
Notes |
| LangSmith |
Click Here |
Python SDK for tracing, evals, and monitoring LLM apps |
| Comet Opik |
Click Here |
Open-source Python library for LLM observability & evals |
| Promptfoo |
Click Here |
Prompt testing & red-teaming, works with Python pipelines |
| Weights & Biases Weave |
Click Here |
Python-native tracking & evaluation for LLM apps |
Running Local & Open-Source LLMs with Python 🖥️
| Resource Name |
Link |
Notes |
| Ollama Python Library |
Click Here |
Official Python client for running local models via Ollama |
| llama-cpp-python |
Click Here |
Python bindings for llama.cpp, run GGUF models directly |
| vLLM |
Click Here |
Python library for high-throughput LLM inference & serving |
| Hugging Face Transformers |
Click Here |
The standard Python library for loading & running open models |
| Hugging Face Hub |
Click Here |
Browse open models/datasets to load via the Python huggingface_hub library |
Build an LLM From Scratch in Python 🔬
For people who want to go past "calling an API" and actually understand what's happening inside the model.
| Resource Name |
Link |
Notes |
| Build a Large Language Model (From Scratch) : Raschka |
Click Here |
Companion Python/PyTorch repo; builds tokenizer → attention → GPT → training loop by hand |
| nanoGPT : Karpathy |
Click Here |
~300-line, dependency-light GPT training/inference in pure PyTorch |
| Neural Networks: Zero to Hero :Karpathy |
Click Here |
Video series that codes backprop, a tokenizer, and a GPT from raw Python |
| Let's Build the GPT Tokenizer :Karpathy |
Click Here |
Builds a BPE tokenizer in Python line by line |
| Annotated Transformer : Harvard NLP |
Click Here |
"Attention Is All You Need" paper reproduced as runnable PyTorch code |
| CS231n / CS224n course notes |
Click Here |
Stanford's deep learning & NLP course notes, Python-based assignments |
Fine-Tuning LLMs in Python (PEFT, LoRA, QLoRA) 🎛️
| Resource Name |
Link |
Notes |
| Hugging Face PEFT |
Link |
The standard Python library for LoRA/QLoRA and other efficient fine-tuning methods |
| Hugging Face PEFT Docs |
Link |
Task-by-task guides (text, vision, speech) |
| TRL (Transformer Reinforcement Learning) |
Link |
Python library for SFT, DPO, and RLHF-style fine-tuning |
| bitsandbytes |
Link |
4-bit/8-bit quantization used for QLoRA on consumer GPUs |
| Unsloth |
Link |
Faster, lower-memory Python library for fine-tuning open LLMs |
| Axolotl |
Link |
YAML-configured Python fine-tuning framework built on PEFT/TRL |
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