Python Cheat Sheet Review: Key Concepts Refreshed

Built my own RAG (Retrieval-Augmented Generation) system from scratch using Python Worked on the complete pipeline: Extracting text from PDF Splitting into meaningful chunks Creating embeddings using Ollama Storing data in Chroma vector database Retrieving relevant information using similarity search Next step is to integrate an LLM and turn this into a full AI-powered application 💡 #AI #MachineLearning #Python #RAG #Ollama #BuildInPublic

This problem demonstrates how dictionary comprehensions can create key–value pairs dynamically. It highlights how values can be generated from existing data while building a dictionary in a compact and readable way. A very useful technique when transforming datasets. THE ANSWER IS: B #Python #DictionaryComprehension #PythonChallenge #DataStructures #BuildInPublic

Andrej Karpathy published microgpt.py — a GPT in 200 lines of pure Python. No PyTorch. No dependencies. Just raw autograd and attention built from scratch. It's a useful way to see exactly what's happening inside a language model: the matrix multiplications, the gradient flow, the attention mechanism in actual code. We walked through it for developers — what each function does, why it works, and when you'd build from scratch vs call an API. Full breakdown: https://lnkd.in/gmNbi5kQ

SkillCourse Day 5/30: Mastering User Interaction in Python I just wrapped up Day 5 of the "30 Days of Python with AI" challenge by Satish Dhawale sir! Today was all about making programs interactive. Key takeaways: The input() function: Learning how to capture user data. Type Casting: Why converting strings to int() or float() is crucial for calculations (no more 1 + 1 = 11 errors!). Data Integrity: Understanding how Python handles different data types during input. #Python #CodingChallenge #AI #LearningInPublic #SatishDhawale #DataAnalyst

Building a deep research agent with Ivan Leo right now. Python, Pydantic, and APIs are all you need. Thinking of this structure: 1.⁠ ⁠User prompt 2.⁠ ⁠Agent asks clarifying questions if needed 3.⁠ ⁠Planner writes research plan 4.⁠ ⁠Main agent orchestrates subagents with individual questions/topics 5.⁠ ⁠Subagents return answers + sources (the context eng bit) 6.⁠ ⁠Main agent synthesizes into final report Would you add/delete anything? Come build this with us in a few days. Register to join live or get the recording afterwards: https://luma.com/u9osjjbn

📈 Day 2/30: Diving Deeper into Python for AI Progress update: Today was all about solidifying Python fundamentals with a focus on AI applications. What I Covered: ✔️ Functions and modules, how to structure code for scalability. ✔️ Working with APIs, practiced making requests to Hugging Face’s inference API. ✔️ Debugging, because breaking code is the best way to learn. Key Takeaway: Writing clean, modular Python isn’t just about syntax, it’s about preparing for complex AI workflows. Question for You: What’s one Python trick or best practice that saved you hours of debugging? Share below! 👇 #Python #AI #30DayChallenge #CodingJourney #TechSkills

Sharpened my Python fundamentals today by diving deep into Dictionaries and Sets. Here’s what I practiced: • Creating dictionaries and sets • Understanding key-value data structures • Dictionary methods and operations • Set methods and real-world use cases • Hands-on practice problems to strengthen concepts Building strong foundations one concept at a time. Consistency > intensity. #Python #CodingJourney #LearningPython #DataStructures #Upskilling #TechSkills

Recently I attended a webinar on Python 🐍 with AI 🧠 by TOPS Technologies 🧑💻 . Honestly, it was a really interesting session 👌. 👉 I got to understand how Python is actually used in AI and real-world applications. ✍ Still learning, but moving step by step 👍 #Python #AI #LearningJourney

🚀 Day 2 of my AI & Data Science Journey Today I learned some important basics of Python 🐍 • What are Data Types (int, float, string, boolean) • How to use Variables to store values • Different types of Operators • Type Casting (converting one data type into another) Slowly understanding how coding actually works 💻 Small steps, but moving forward every day 📈 #Day2 #Python #LearningJourney #DataScience #Beginner #Consistency #AI

🐍 Day 89 — Features and Labels Day 89 of #python365ai 📌 Features (X) → input variables Labels (y) → output Example: X = [size, rooms] y = price 📌 Why this matters: Clear distinction is essential for building ML models. 📘 Practice task: Identify features and labels in a dataset. #python365ai #Features #MachineLearning #Python