Learning Build Local AI Apps with Ollama, Python, and Streamlit

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Build Local AI Apps with Ollama, Python, and Streamlit
Published 7/2026
Created by School of AI, Arjun Vaid
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 42 Lectures ( 7h 29m ) | Size: 7 GB
Build private, production-ready AI apps locally with Ollama, Python, Streamlit, RAG, memory, tools, and real projects

What you'll learn
Install Ollama and run large language models locally on a computer.
Connect Python applications to Ollama models.
Build interactive AI applications with Streamlit.
Create chatbots with memory, personas, and conversation history.
Process PDF and text documents for local AI workflows.
Generate embeddings and build semantic search with ChromaDB.
Create Retrieval-Augmented Generation applications with citations.
Generate structured JSON outputs, summaries, flashcards, and quizzes.
Build tool-using AI applications and research workflows.
Evaluate, optimize, and manage production-ready local AI applications.
Requirements
Basic computer skills and confidence installing software.
A Windows, macOS, or Linux computer capable of running Ollama.
At least 8 GB of RAM; 16 GB or more is recommended.
Sufficient free storage for downloading local AI models.
Basic Python knowledge is helpful but not required.
No previous machine learning or AI experience is necessary.
No paid AI API subscription is required.
A code editor such as Visual Studio Code is recommended.
An internet connection is needed initially to install tools and models.
A willingness to follow practical exercises and experiment with code.
Description
This course contains the use of artificial intelligence.
Build powerful, private, and practical AI applications directly on your own computer withOllama,Python, andStreamlit.
This hands-on course teaches you how to create completelocal AI apps without depending on paid AI APIs or sending sensitive information to external cloud services. You will learn how to runlarge language models locally, connect them to Python, design effective prompts, build interactive user interfaces, and transform simple ideas into useful AI applications.
The course begins with the foundations oflocal artificial intelligence. You will install Ollama, download and manage local models, adjust model parameters, write system instructions, and connect your models to Python applications. You will then use Streamlit to create clean, browser-based interfaces that make your AI projects easy to use and demonstrate.
As you progress, you will build conversational applications withchat history,memory, customizable personas, session state, conversation exports, and reset controls. You will also learn how to process PDF and text documents, clean and chunk content, generatelocal embeddings, store vectors inChromaDB, and performsemantic search across private knowledge.
Next, you will build completeRetrieval-Augmented Generation, orRAG, applications. You will retrieve relevant document sections, construct grounded prompts, generate answers with sources and page references, reduce hallucinations, and handle questions that are not supported by the available documents.
The course also coversstructured AI outputs, reusable prompt templates, JSON generation, summarization, flashcards, quizzes, progress tracking, and learning applications. You will then move intotool-using AI, where models can call Python functions, organize research tasks, compare evidence, track references, and produce structured research reports.
Every day includes a practical, industry-focused project. You will build aFactory Operations Assistant, aHotel Guest-Service Chatbot, aLegal Contract Clause Finder, aBanking Compliance Assistant, anAircraft Technician Training Assistant, aDrug Research Assistant, and aRetail Operations AI Control Center.
These projects demonstrate how local AI can support manufacturing, hospitality, legal services, financial services, aviation, pharmaceuticals, and retail while improving privacy and data control.
You do not need to be an AI expert to begin. Each concept is explained progressively, with practical examples that connect technical ideas to working applications. Instead of learning isolated theory, you will understand why each component matters, how the pieces work together, and how to troubleshoot common problems. The course helps you build confidence with local models, Python workflows, document pipelines, vector databases, and interactive application development.
By the end of the course, you will understand how to structure multi-page Streamlit applications, manage models and documents, optimize performance, implement logging and validation, evaluate retrieval quality, compare model responses, rebuild vector indexes, and monitor user feedback.
This course is ideal for beginners, Python learners, developers, AI enthusiasts, business professionals, and anyone who wants to buildprivate AI applications,offline AI tools,document Q&A systems,AI chatbots, andproduction-ready local AI solutions.
Start with one local model, build one simple interface, and finish with a portfolio of seven real-world AI applications you can customize, demonstrate, and expand for your career, portfolio, future business, or organization.
Who this course is for
Beginners who want to start building practical AI applications.
Python learners looking for real-world AI projects.
Developers who want to build private, local-first AI solutions.
AI enthusiasts interested in Ollama and open local models.
Professionals working with confidential or sensitive documents.
Students building an AI application portfolio.
Business analysts and consultants exploring AI use cases.
Educators creating AI-powered learning and training tools.
Teams evaluating alternatives to cloud-based AI services.
Entrepreneurs who want to prototype AI products without paid APIs.
Homepage

https://www.udemy.com/course/build-local-ai-apps-with-ollama-python-and-streamlit

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