1. What Is Ollama? Ollama is a lightweight runtime that allows you to run large language models (LLMs) locally on your own machine. Instead of calling cloud-based APIs (like OpenAI or Anthropic), Ollama enables you to download open-source models and perform inference completely offline. In simple terms: 2. Why Ollama Exists Most Generative AI tutorials…

1. Introduction: What Does Linear Regression Try to Achieve? Linear regression is one of the most fundamental tools in statistics and machine learning. Its purpose is simple—but extremely powerful: To model how multiple input variables influence a single output, using a linear equation. This makes it useful in a huge range of fields—economics, finance, engineering,…

Linear regression is one of the most fundamental and widely used algorithms in machine learning. If you are beginning your ML journey, this is the perfect starting point because it teaches you how models learn relationships, make predictions, and optimize accuracy. In this tutorial, we will break down the concepts behind linear regression, understand where…

1. Prerequisites and Setup In the previous video, we: Example .env (already created earlier): 1.1 Required Python packages Make sure your requirements.txt has at least: Then install: ipykernel is needed so Jupyter can run this environment as a kernel. 2. Loading Environment Variables in Python First, we load variables from .env and ensure they’re available…

Vector databases play a crucial role in modern LLM-powered applications. Whenever we want to store, search, or retrieve information semantically (using meaning instead of keywords), we rely on vector stores. In this tutorial, we will focus on ChromaDB, one of the most popular and developer-friendly open-source vector databases. This guide is written in simple language…

In modern RAG (Retrieval-Augmented Generation) systems, embeddings and vector stores are core components.This tutorial walks you step-by-step through: We will use open-source embeddings and run everything locally with no external API requirements. 1. Understanding Vector Stores A vector store is a special database designed to store high-dimensional vectors (embeddings).It enables fast similarity search and is…

In this tutorial, you’ll learn how to: This is one of the key building blocks in a RAG (Retrieval-Augmented Generation) or search system: converting text into vectors (embeddings) that capture semantic meaning. 1. What Are Embeddings (Quick Recap)? Embeddings are numeric representations of text (usually large vectors of floats). Texts that are semantically similar (e.g.,…

In the previous sessions, you learned: We now move to Step 3, where you convert these text chunks into vector embeddings.These embeddings are the foundation of modern retrieval systems, semantic search, and RAG (Retrieval-Augmented Generation) applications. This tutorial focuses entirely on OpenAI embeddings. What Are Embeddings? Embeddings are one of the most important concepts in…

When working with real-world APIs, we often receive large JSON responses that contain deeply nested objects, arrays, and long fields. Before we send this data to an LLM or convert it into embeddings for retrieval, we must break it into smaller and meaningful chunks. However, depending on your LangChain version, the built-in RecursiveJsonSplitter may not…

In earlier lessons, we explored several text-splitting techniques in LangChain. In this tutorial, we will focus on a powerful and lesser-known utility: the HTML Header Text Splitter. This splitter helps you break down HTML documents into logical, structured chunks based on the hierarchy of HTML header tags such as <h1>, <h2>, and <h3>. This becomes…