https://teknolabs.net/courses/python/advanced-python/index.html 🔥 Advanced Python Course Introduction Welcome to the Advanced Python course — a hands-on, project-driven journey designed to elevate your Python skills and unlock the powerful tools used in modern software development, data science, and AI integration. In this course, we go beyond the basics and explore the powerful ecosystem around Python that enables you to build APIs, web applications, automation tools, and machine learning-powered systems with real-world relevance. 🧠 What You’ll Learn Throughout the course, you’ll dive deep into the following advanced topics: Hugo en-us https://teknolabs.net/courses/python/advanced-python/1-pydantic/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/1-pydantic/index.html Python’s Dynamic Typing Problem How To Use Pydantic Validating Data with Pydantic Custom Field Validation JSON Serialization Pydantic vs Dataclasses Python’s Dynamic Typing Problem One of the biggest issues with Python as a programming language is the lack of static typing. Python uses dynamic typing , which means that when you create a variable you don’t have to declare it is type, example: # Python x = 10 # Java int x = 10; Once a Python variable is created, you can also override it with a different type: https://teknolabs.net/courses/python/advanced-python/2-requests/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/2-requests/index.html Introduction GET Request HTTP Status Codes Request Content POST Request Handling Errors Setting a Timeout HTTP Request Headers Web Scraping with BeautifulSoup Requests vs urllib Introduction Requests is an elegant and simple HTTP library for Python, built for human beings (HTTP fir Humans) The Requests module in Python lets you make HTTP requests so thant can interact with any website or API directly from your Python app. GET Request To use Requests Python module, we have to install it: pip install requests To make a simple GET request to a website, use this code: import requests url = "https://www. https://teknolabs.net/courses/python/advanced-python/3-fastapi/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/3-fastapi/index.html Why Use FastAPI? Install and Get Started with FastAPI GET and POST Routes Handling HTTP Errors JSON Request and Path Parameters Pydantic models Response Models Interactive Documentation FastAPI vs Flask vs Django Why Use FastAPI? Easy to learn Fast development High performance (async by default) Install and Get Started with FastAPI To install FastAPI, open the terminal and run the following command: pip install fastapi uvicorn Uvicorn is the server that will be used ti test and run our FastAPI applications. https://teknolabs.net/courses/python/advanced-python/4-streamlit/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/4-streamlit/index.html What is Streamlit? Using Input Elements Working with Data Multipage Apps Loan Repayments App Deploying to Streamlit Cloud What is Streamlit? Streamlit is an open-source Python library that makes it easy to create and share beautiful custom web apps for machine learning and data science. To install Streamlit: pip install streamlit To test it: import streamlit as st st.write('Hello World') To run the example: streamlit run main.py How streamlit works is that it evaluate your file from top to bottom and render each thing in the order that they appear. https://teknolabs.net/courses/python/advanced-python/5-fastapi-postgresql/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/5-fastapi-postgresql/index.html Introduction FastAPI, SQLAlchemy, uvicorn, pydantic installation Start FastAPI Application Create PostgreSQL connection Create PostgreSQL Tables Connect FastAPI, PostgreSQL with SQLAlchemy FastAPI API Add Question Endpoint Other FastAPI API Endpoints In this FastAPI with PostgreSQL Tutorial, we’ll delve into FastAPI, a powerful python web framework and SQLAlchemy, a SQL toolkit that enhances the flexibility of SQL. Introduction In this project, we will create a QUIZ Game, we will setup FastAPI with PostgreSQL. https://teknolabs.net/courses/python/advanced-python/6-web-scraping/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/6-web-scraping/index.html Lab Objectives Make an HTTP request Parse the HTTP response with “Beautifulsoup” Extract individual comments Clean up the response text Process the scraped content for useful data Visualizing the data with “matplotlib” What Next? Mini Project to do in pairs Lab Objectives Create a Pyhton script to scrape job postings from a forum post. Analyse the data to see how popular different technologies are. [!IMPORTANT] > Web Scraping Ethics https://teknolabs.net/courses/python/advanced-python/7-langchain/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/7-langchain/index.html Introduction to Langchain Setting Up Langchain and Groq Getting API Access MODEL I/O Generate Predictions Prompt Templates Getting Structured Output Building an AI Agent Other Langchain Features _ Retrieval Augmented Generation (RAG) _ Memory Module Introduction to Langchain Langchain is an AI toolkit that lets you build complex application on top of LLMs, like chatGPT, DeepSeek, Claude. You can use them to create templates: prompt_template = PromptTemplate.from_template( "List {n} cooking/meal titles for {cuisine} cuisine. https://teknolabs.net/courses/python/advanced-python/8-crawl4ai/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/8-crawl4ai/index.html What is Crawl4AI Why Crawl4AI? Basic Crawl4AI Example - Single Page Crawl Ethics of Web Scraping Crawling Multiple Pages FAST Parallel Page Crawling What is Crawl4AI Crawl4AI is an open-source web crawling framework specifically designed to scrape websites and format the output in the best way for LLMs to understand. In this tutorial, we will use Crawl4AI to scrape a website for LLM. Why Crawl4AI? Built for LLMs: Creates smart, concise Markdown optimized for RAG and fine-tuning applications. https://teknolabs.net/courses/python/advanced-python/9-llm-access-fastapi-ollama/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/9-llm-access-fastapi-ollama/index.html Objective Prerequisites Lab Setup Step 1: Install Python and pip Step 2: Install Required Libraries Step 3: Set Up Ollama Lab Steps Step 1: Create a Simple FastAPI Application Step 2: Test the Unsecured API Step 3: Secure the API with an API Key Step 4: Test the Secured API Step 5: Implement a Credit System (Optional) Step 6: Deploy the API to the Cloud (Advanced Optional) Deliverables Assessment Criteria Conclusion This lab demonstrate how to create a secure API using Python, FastAPI, and Ollama to control access to a Large Language Model (LLM) running locally. https://teknolabs.net/courses/python/advanced-python/10-machine-learning/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/10-machine-learning/index.html Lab Overview Lab Structure Part 1: Building the Machine Learning Model Part 2: Version Control with Git Part 3: Creating a Prediction Service with FastAPI Part 4: Containerization with Docker Part 5: CI/CD Pipeline with GitHub Actions Part 6: Cloud Deployment and Monitoring (Using Render) Optional Challenges This lab combines machine learning with DevOps and cloud technologies. You will build a machine learning model to predict music genres based on user age and gender, create a prediction service using FastAPI, containerize it with Docker, set up a CI/CD pipeline with GitHub Actions, and deploy it to Render, a free cloud provider. https://teknolabs.net/courses/python/advanced-python/11-mcp-with-fastapi/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://teknolabs.net/courses/python/advanced-python/11-mcp-with-fastapi/index.html Objective: In this lab, you will create a product catalog API using FastAPI and transform it into an AI-accessible service using the Model Context Protocol (MCP) with FastMCP. The lab is divided into two parts: Part 1 focuses on building and testing a FastAPI-based product catalog API, and Part 2 guides you through creating an MCP server to expose your API as AI-callable tools. Prerequisites: Python 3.10+ installed. Basic understanding of Python, REST APIs, and JSON.