#Google Colab

Hands-on guide to run PySpark in Colab, perform ETL, run SQL and window functions, train a logistic regression model, and save results to Parquet.

'Step-by-step tutorial to create an AI desktop agent that interprets natural language, simulates file/browser/system tasks, and runs an interactive Colab demo.'

'Run MATLAB-style Octave code directly from Python using oct2py: exchange arrays, call .m functions, plot Octave figures in Colab, and use Octave toolboxes seamlessly.'

'Create a Colab-ready BioPython AI Agent that automates sequence retrieval, analysis, visualization, and phylogenetic tree building using Biopython and popular Python tools.'

'Hands-on tutorial for a full Gensim-based NLP pipeline: preprocessing, LDA topics, Word2Vec embeddings, TF-IDF similarity and semantic search, ready for Colab.'

'Step-by-step overview of a Colab-ready multi-round research system using Gemini for extraction and DuckDuckGo for quick web snippets, culminating in automated reports.'

Learn how to build a multi-agent conversational AI framework by integrating Microsoft AutoGen with the free Gemini API. This tutorial covers creating specialized AI agent teams to autonomously manage research, business analysis, and software development projects.

A detailed tutorial on setting up a multi-agent AI pipeline with CrewAI and Google Gemini in Colab, featuring research, analysis, and content creation agents working together.

Explore how Modin can supercharge your pandas workflows with parallel computing on Google Colab. See benchmarks across complex data operations and learn best practices to optimize performance.

This tutorial guides you through building and analyzing a complex Alzheimer's disease knowledge graph using PyBEL within Google Colab, including advanced network metrics and visualization.

'Learn how to build a secure, multi-tool AI agent in Google Colab by integrating Riza’s sandboxed Python execution with Google Gemini’s generative model using LangChain tools.'

Explore an advanced Python tutorial combining SerpAPI and Google Gemini-1.5-Flash to perform enriched web searches and AI-powered analysis for comprehensive research workflows.

This guide explains how to fine-tune the Qwen3-14B model efficiently on Google Colab with Unsloth AI, leveraging 4-bit quantization and LoRA for memory-efficient training using mixed reasoning and instruction datasets.

This tutorial explains how to build a memory-enabled chatbot combining Anthropic's Claude model with mem0 for context-rich conversations in Google Colab.

Discover a practical tutorial on implementing the Model Context Protocol to manage context effectively for large language models using semantic chunking and dynamic token management.