Building with LLMs in Python: A Step-by-Step Learning Path

Build your first AI agent with Python and Claude Learn to build an AI agent with Python and Claude that uses tools, makes decisions, and executes multi-step tasks autonomously. Read the full post 👇 https://lnkd.in/ghMddf5c #GenerativeAI #AI #WebDevelopment #PHP #Python #Developer #LLM

Users of Python Fundamentals, 2/e on O'Reilly: The new Lesson 5, Lists and Tuples, is live, and I am sending Lesson 06, Dictionaries and Sets, for processing right now. Should be live before the end of the week. https://lnkd.in/ePMpTP5t #Python Pearson Deitel & Associates

Today was a valuable learning experience focused on AI with Python. I developed a project that analyzes resumes against job descriptions, providing an analysis with a matching score. In addition, I created a simple user interface using Streamlit and Markdown in Python, utilizing IDE Visual Studio Code along with langchain, Vector db Chroma, Embedding, and Google Gemini. Here are the main AI tools used: - Loaders: from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader - Splitters: from langchain_text_splitters import RecursiveCharacterTextSplitter - Vector database: from langchain_community.vectorstores import Chroma - Gemini model: from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings - Embedding model: embedding = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-001") - LLM Model: llm = ChatGoogleGenerativeAI(model="models/gemini-2.5-flash", temperature=0.3)

Python was the first programming language I learned, but for me it fell by the wayside years ago. I’m now re-learning it specifically because it seems to be a required skill in the new generation of “AI” companies. So - genuine question for technical folks building AI companies: If your backend is just routing prompts to Anthropic or OpenAI — you're not doing ML. You're doing API calls. So why Python? If you're not training models, if you're not running local inference, you have no NumPy pipelines or CUDA kernels…why on earth Python? Golang gives you compiled performance, tiny binaries, and dead-simple concurrency. Node/TypeScript unifies your entire engineering team under one language and toolchain. There are plenty of other options. Python made sense when once upon a time but now? Not so sure. If your company adds value while still being essentially an AI passthrough - is your stack a technical decision? 

👉 PYTHON FOR AI Python didn’t become the default for AI because it’s easy. It became default because it fits into the entire AI lifecycle. 👉 AI is not just about training a model. It’s about moving data, invoking models, handling outputs, and integrating systems. That’s where Python becomes critical. 👉 What makes Python critical in AI systems: • Interface layer → Interacts with models, APIs, and external services • Data layer → Handles preprocessing, transformations, and pipelines • Control layer → Manages workflows, decisions, and orchestration 👉 Most discussions stop at frameworks. But in real-world systems, Python is doing much more: • Structuring inputs before they reach the model • Managing responses after the model generates output • Connecting AI with applications, databases, and tools 👉 Key Insight: Python doesn’t just build models — it connects models to real-world systems. #Python #PythonForAI #AIEngineering #SystemDesign #LearningInPublic #GenAIJourney

💬 Task 8: Simple Chatbot (CLI) – Python Project Created a basic rule-based chatbot using Python 🐍 that interacts through the command line interface (CLI). ✅ Features: • Responds to greetings like “Hi”, “Hello” 👋 • Handles simple FAQs 🤔 • Uses if-elif conditions for conversation flow • Provides quick and interactive responses 💡 What I learned: • Logic building using conditional statements • Handling user input effectively • Designing basic conversational flow • Improving problem-solving skills 🚀 Outcome: A beginner-friendly chatbot that simulates simple human conversation and builds a strong foundation for advanced AI/ML chatbot development. 📌 Small steps today, smarter systems tomorrow! #Python #Chatbot #Coding #BeginnerProjects #AI #LearningJourney #100DaysOfCode

🐍📰 In this tutorial, you'll learn all about the k-Nearest Neighbors (kNN) algorithm in Python, including how to implement kNN from scratch, kNN hyperparameter tuning, and improving kNN performance using bagging #python

Why learn it? Python is considered the "lingua franca" of the artificial intelligence industry and a fundamental requirement for most AI-related job roles. While other languages like C++ are used for high-performance backends, Python serves as the primary interface for developing, testing, and deploying models. Take this online course to hone your AI skills or upskill. #C++

🚀 Just published a new article on Generative AI in Python. I’ve covered how to build production-ready systems using concepts like FastAPI, RAG, and performance optimization. If you're into Python, backend development, or AI, this might be useful. #GenerativeAI #Python #SoftwareEngineering

In this article, you will learn how to build a local, privacy-first tool-calling agent using the Gemma 4 model family and Ollama. Topics we will cover include: An overview of the Gemma 4 model family and its capabilities. How tool calling enables language models to interact with external functions. How to implement a local tool calling system using Python and Ollama. https://lnkd.in/d6Wa86Gx