Mastering Ollama: Build Private Local LLM Apps with Python from Udemy

Are you concerned about data privacy and the high costs associated with using Large Language Models (LLMs)?

If so, this course is the perfect fit for you. "Mastering Ollama: Build Private LLM Applications with Python" empowers you to run powerful AI models directly on your own system, ensuring complete data privacy and eliminating the need for expensive cloud services.

By learning to deploy and customize local LLMs with Ollama, you'll maintain full control over your data and applications while avoiding the ongoing expenses and potential risks of cloud-based solutions.

This hands-on course will take you from beginner to expert in using Ollama, a platform designed for running local LLM models. You'll learn how to set up and customize models, create a ChatGPT-like interface, and build private applications using Python—all from the comfort of your system.

In this course, you will:

Install and configure Ollama for local LLM model execution.
Customize LLM models to suit your specific needs using Ollama’s tools.
Master command-line tools to control, monitor, and troubleshoot Ollama models.
Integrate various models, including text, vision, and code-generating models, and even create your custom models.
Build Python applications that interface with Ollama models using its native library and OpenAI API compatibility.
Develop Retrieval-Augmented Generation (RAG) applications by integrating Ollama models with LangChain.
Implement tools and function calling to enhance model interactions in terminal and LangChain environments.
Set up a user-friendly UI frontend to allow users to chat with different Ollama models.

Why is this course important?

In a world where data privacy is growing, running LLMs locally ensures your data stays on your machine. This enhances data security and allows you to customize models for specialized tasks without external dependencies or additional costs.

You'll engage in practical activities like building custom models, developing RAG applications that retrieve and respond to user queries based on your data, and creating interactive interfaces.

Each section has real-world applications to give you the experience and confidence to build your local LLM solutions.

Why choose this course?

This course is uniquely crafted to make advanced AI concepts approachable and actionable. We focus on practical, hands-on learning, enabling you to build real-world solutions from day one. You'll dive deep into projects that bridge theory and practice, ensuring you gain tangible skills in developing local LLM applications. Whether you're new to large language models or seeking to enhance your existing abilities, this course provides all the guidance and tools you need to create private AI applications using Ollama and Python confidently.

Ready to develop powerful AI applications while keeping your data completely private?

Enroll today and seize full control of your AI journey with Ollama.

Harness the capabilities of local LLMs on your own system and take your skills to the next level.

What's inside

Learning objectives

Install and configure ollama on your local system to run large language models privately.
Customize llm models to suit specific needs using ollama’s options and command-line tools.
Execute all terminal commands necessary to control, monitor, and troubleshoot ollama models.
Set up and manage a chatgpt-like interface, allowing you to interact with models locally.
Utilize different model types—including text, vision, and code-generating models—for various applications.

Create custom llm models from a modelfile file and integrate them into your applications.
Build python applications that interface with ollama models using its native library and openai api compatibility.
Develop retrieval-augmented generation (rag) applications by integrating ollama models with langchain.
Implement tools and function calling to enhance model interactions for advanced workflows.
Set up a user-friendly ui frontend to allow users to interface and chat with different ollama models.

Install and configure ollama on your local system to run large language models privately.
Customize llm models to suit specific needs using ollama’s options and command-line tools.
Execute all terminal commands necessary to control, monitor, and troubleshoot ollama models.
Set up and manage a chatgpt-like interface, allowing you to interact with models locally.
Utilize different model types—including text, vision, and code-generating models—for various applications.
Create custom llm models from a modelfile file and integrate them into your applications.
Build python applications that interface with ollama models using its native library and openai api compatibility.
Develop retrieval-augmented generation (rag) applications by integrating ollama models with langchain.
Implement tools and function calling to enhance model interactions for advanced workflows.
Set up a user-friendly ui frontend to allow users to interface and chat with different ollama models.

Syllabus

Introduction

Introduction & What Will Your Learn

Course Prerequisites

Please WATCH this DEMO

Development Environment Setup

Udemy 101 - Tips for A Better Learning Experience

Download Code and Resources

How to Get Source Code

Download Source code and Resources

Ollama Deep Dive - Introduction to Ollama and Setup

Ollama Deep Dive - Ollama Overview - What is Ollama and Advantages

Ollama Key Features and Use Cases

System Requirements & Ollama Setup - Overview

Download and Setup Ollama and Llam3.2 Model - Hands-on & Testing

Ollama Models Page - Full Overview

Ollama Model Parameters Deep Dive

Understanding Parameters and Disk Size and Computational Resources Needed

Ollama CLI Commands and the REST API - Hands-on

Ollama Commands - Pull and Testing a Model

Pull in the Llava Multimodal Model and Caption an Image

Summarization and Sentiment Analysis & Customizing Our Model with the Modelfile

Ollama REST API - Generate and Chat Endpoints

Ollama REST API - Request JSON Mode

Ollama Models Support Different Tasks - Summary

Ollama - User Interfaces for Ollama Models

Different Ways to Interact with Ollama Models - Overview

Ollama Model Running Under Msty App - Frontend Tool - RAG System Chat with Docs

Ollama Python Library - Using Python to Interact with Ollama Models

The Ollama Python Library for Building LLM Local Applications - Overview

Interact with Llama3 in Python Using Ollama REST API - Hands-on

Ollama Python Library - Chatting with a Model

Chat Example with Streaming

Using Ollama show Function

Create a Custom Model in Code

Ollama Building LLM Applications with Ollama Models

Hands-on: Build a LLM App - Grocery List Categorizer

Building RAG Systems with Ollama - RAG & LangChain Overview

Deep Dive into Vectorstore and Embeddings - The Whole Picture - Crash course

PDF RAG System Overview - What we'll Build

Setup RAG System - Document Ingestion & Vector Database Creation and Embeddings

RAG System - Retrieval and Querying

RAG System - Cleaner Code

RAG System - Streamlit UI

Ollama Tool Function Calling - Hands-on

Function Calling (Tools) Overview

Setup Tool Function Calling Application

Categorize Items Using the Model and Setup the Tools List

Tools Calling LLM Application - Final Product

Final RAG System with Ollama and Voice Response

Voice RAG System - Overview

Setup EleveLabs API Key and Load and Summarize the Document

Ollama Voice RAG System - Working!

Adding ElevenLab Voice Generated Reading the Response Back to Us

Wrap up

Wrap up - What's Next?

Bonus Lecture

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Focuses on running LLMs locally, which ensures data stays on your machine and enhances data security, which is ideal for privacy-conscious developers

Teaches how to build Python applications that interface with Ollama models using its native library and OpenAI API compatibility, which is useful for developers

Develops Retrieval-Augmented Generation (RAG) applications by integrating Ollama models with LangChain, which is a popular framework for building LLM-powered applications

Crafted to make advanced AI concepts approachable and actionable, enabling you to build real-world solutions from day one, which is great for beginners

Explores customizing LLM models to suit specific needs using Ollama’s options and command-line tools, which gives learners greater control over model behavior

Covers setting up a user-friendly UI frontend to allow users to interface and chat with different Ollama models, which is useful for creating interactive applications

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mastering Ollama: Build Private Local LLM Apps with Python with these activities:

Review Python Fundamentals

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Strengthen your Python foundation to better understand the code examples and build your own applications using Ollama's Python library.

Browse courses on Python Basics

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Familiarize yourself with common Python libraries.
Review basic data types, control flow, and functions.
Practice writing simple Python scripts.

Brush up on REST API concepts

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Revisit REST API principles to effectively use Ollama's REST API for generating and chatting with models.

Browse courses on REST API

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Understand the basics of HTTP methods (GET, POST, PUT, DELETE).
Learn about request and response formats (JSON).
Explore API authentication methods.

Read 'LangChain in Motion'

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Deepen your understanding of LangChain to build more sophisticated RAG applications with Ollama.

View Putting Knowledge to Work on Amazon

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Read the chapters related to RAG and integrations.
Experiment with the code examples provided in the book.

Four other activities

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Show all seven activities

Follow LangChain tutorials

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Enhance your practical skills in building RAG systems by following online tutorials that demonstrate LangChain's capabilities.

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Find tutorials on building RAG applications with LangChain.
Implement the steps outlined in the tutorials.
Adapt the tutorials to work with Ollama models.

Read 'Natural Language Processing with Python'

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Gain a deeper understanding of NLP principles to enhance your ability to customize and fine-tune LLMs within Ollama.

View Natural Language Processing with Python:... on Amazon

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Focus on chapters related to text processing and language modeling.
Experiment with the code examples provided in the book.

Build a custom chatbot

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Solidify your understanding of Ollama and Python by creating a chatbot that interacts with a local LLM.

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Design the chatbot's functionality and user interface.
Implement the chatbot using Python and Ollama's API.
Test and refine the chatbot's performance.

Contribute to Ollama documentation

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Deepen your understanding of Ollama by contributing to its open-source documentation, helping other users learn and use the platform effectively.

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Identify areas in the documentation that need improvement.
Submit pull requests with your proposed changes.
Respond to feedback from the Ollama community.

Career center

Learners who complete Mastering Ollama: Build Private Local LLM Apps with Python will develop knowledge and skills that may be useful to these careers:

Artificial Intelligence Developer

Artificial intelligence developers create intelligent applications using machine learning models, which makes this course especially helpful. This role requires not just an understanding of models, but also how to integrate them into different applications. This course, focused on Ollama and local LLM deployment, provides practical experience by teaching how to set up and customize models. The course teaches how to use Python for interfacing with models, which is an important skill for an AI developer. This course's focus on practical, hands-on learning, enables AI developers to build real-world solutions, such as RAG applications and custom models. A core skill taught in the course is using a models' native library, as well as interacting with a model through the OpenAI API. This course will be very useful in gaining direct experience with the tools necessary for the role.

See salaries and explore the career path for Artificial Intelligence Developer

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models, often requiring a deep understanding of local model execution, a key focus of this course. This role involves working with different types of models, such as text, vision, and code-generating models. This course, designed for building private applications with Ollama, helps engineers learn to customize models and build applications with them. This course teaches how to integrate different models, a key skill for machine learning engineers. The course goes in depth on using Python for application development, as well as using tools such as LangChain, which is a practical skill for anyone in this role. The focus on building Retrieval-Augmented Generation applications is also highly relevant, as a machine learning engineer often has to ensure that models can respond to user queries on user data.

See salaries and explore the career path for Machine Learning Engineer

Natural Language Processing Engineer

A natural language processing engineer focuses on developing systems that can understand and generate human language. The engineer will find this course may be useful in learning to work with local LLMs using Ollama. This course teaches how to customize local LLMs which is highly relevant for NLP engineers who need to tailor models to specific language tasks. This course also provides hands-on experience in building Python applications that interface with LLMs as well as developing RAG applications, which are important in NLP. The course also teaches how to use command line tools to manage and monitor models which is a practical skill for NLP engineers. The focus on building interactive interfaces with models, as taught in this course, is also useful. This course may help engineers to leverage local LLMs in their work.

See salaries and explore the career path for Natural Language Processing Engineer

Data Scientist

A data scientist works with large datasets to extract insights and build predictive models, requiring a grasp of how local models function and how to tailor them, a key focus of this course. This course teaches using Ollama, a platform for running local LLM models, which provides a private and cost effective approach to working with potentially sensitive data. This course goes into integrating different types of machine learning models, which is a crucial aspect of a data scientist's role. The data scientist can apply what’s learned about RAG applications directly to their own projects. The course's hands on approach helps prepare the data scientist to work with LLMs. The course also covers the Python library, as well as leveraging the OpenAI API. This experience helps to ensure that a data scientist is proficient in these tools.

See salaries and explore the career path for Data Scientist

Software Engineer

Software engineers design and develop software applications, and this course may be useful as they gain expertise in integrating AI functionalities into their applications. This course provides hands on practical skills for setting up local LLM models, which software engineers can leverage to create private and cost effective solutions. Understanding how to use Python to interface with these models is crucial, as it allows software engineers to integrate AI capabilities into their software. This course directly addresses these needs by teaching the use of the Ollama Python library and how to build applications with an interactive user interface. The course also teaches how to build RAG applications, which can be very valuable in building comprehensive software applications that leverage user data.

See salaries and explore the career path for Software Engineer

AI Solutions Architect

An AI solutions architect designs and oversees the implementation of AI systems, which means this course may be useful in gaining hands-on experience with local LLM models. This course, focusing on mastering Ollama, provides an in depth look at deploying and customizing models, which is useful knowledge for architects. An architect needs to be able to develop private applications using Python, and this course focuses on using the native library as well as the OpenAI API to build Python applications. The course teaches how to implement tools and function calling to enhance model interactions, which is crucial for building integrated AI solutions. The course also discusses how to build applications using the models, as well as integrate these models with LangChain. This course may help to solidify the necessary skills to fulfill the role.

See salaries and explore the career path for AI Solutions Architect

Computer Vision Engineer

A computer vision engineer develops systems that can interpret and understand visual information. This course may be helpful for engineers looking to work with LLMs that can process images locally. The course teaches how to integrate vision models into applications. This course teaches how to use Ollama to run different types of models locally, which is important for computer vision engineers working with vision models and image data. The engineer will learn how to use the Python library, which is also valuable for any computer vision engineer who needs to integrate AI with Python applications. The hands-on approach of the course will allow engineers to build interactive interfaces with their vision models for testing. This course may help computer vision engineers leverage local LLMs.

See salaries and explore the career path for Computer Vision Engineer

Research Scientist

A research scientist often experiments with cutting-edge technologies including artificial intelligence, and may find this course helpful in learning how to deploy and customize local LLMs. This role often requires working with large language models (LLMs) and the course's focus on running them locally can be beneficial to scientists, especially when data privacy is an issue as discussed in the course description. The course provides hands-on experience with tools like Ollama, and command line tools, and also teaches the Python library for interacting with these models. The scientist can apply the course's lessons on model customization and development of private applications to their own research. The course also teaches how to develop RAG applications, which is crucial for research applications.

See salaries and explore the career path for Research Scientist

Robotics Engineer

A robotics engineer designs, builds, and tests robots and robotic systems. The role may require integrating AI capabilities using local LLMs. This course may help engineers learn to deploy and tailor local LLMs, which can be integrated into robotic systems. The course teaches using Ollama, a platform for running these models locally, which is useful for integrating AI functionalities into robotics. This course goes into using Python to interface with models, which is especially important for any engineer who needs to automate functions. This course provides hands on learning in building custom models and creating applications. A robotics engineer will find the lessons on building RAG applications to be useful. This course may offer some advantages in preparing for this role.

See salaries and explore the career path for Robotics Engineer

Data Analyst

A data analyst examines data and generates reports on trends, and may find some value in this course's focus on data privacy and local LLMs. This course teaches how to run LLMs locally using Ollama, which is beneficial for data analysts who need to ensure the privacy of their data. Analysts might find it useful to be able to customize these models to suit their specific needs. This course teaches how to use Python to interact with these models, which is helpful for automating data analysis tasks. The course also discusses using the models for different tasks, which can help a data analyst understand and leverage these models in their work. The focus on RAG systems is another important aspect of the course. Data analysts might find it useful to be able to build applications that retrieve and respond to user queries based on their own data.

See salaries and explore the career path for Data Analyst

Bioinformatics Specialist

A bioinformatics specialist analyzes biological data using computation tools, which may include applying models locally. This course may be helpful for a specialist who needs to ensure data privacy. This course teaches you how to customize models using Ollama, which can be useful in tailoring AI for specific data needs. The course goes deep on how to use Python to build applications, which is an important skill for bioinformatics. The course covers the implementation of tools and function calling, which would be useful for creating workflows and automations in the context of bioinformatics. The course also covers RAG applications, which may be applicable to retrieving information from biological databases. This course may be useful to learn how to apply LLM models in this field.

See salaries and explore the career path for Bioinformatics Specialist

Cloud Computing Engineer

A cloud computing engineer manages and maintains cloud-based infrastructure. This course, focused on local LLMs, may enhance their understanding of AI deployment and architecture. The course teaches how to set up local LLMs using Ollama, which provides a basis for comparison with cloud based AI services. This course goes into using Python to interface with these models. The course focuses on building applications with interactive user interfaces, which may be useful for a cloud computing engineer. The lessons in the course will give a cloud engineer skills that they can leverage to optimize cloud infrastructure costs. The course teaches how to work with RAG applications, which can also be deployed on the cloud. This course may be useful to provide a better understanding of cloud based AI deployments.

See salaries and explore the career path for Cloud Computing Engineer

IT Security Specialist

An IT security specialist ensures the security of computer systems and networks and may find this course helpful in understanding the implications of local LLMs. The course's focus on data privacy and local deployment of LLMs may align with security concerns addressed by an IT specialist. The course teaches how to control access to models and how to customize them, as well as using command line tools, all useful for a specialist to be aware of. The ability to build custom models, as taught in this course, may be useful to a specialist evaluating the security of different systems. An IT specialist may find the details of the course useful for understanding how local LLMs are accessed, and securing data. This course may provide information on how to better approach AI security protocols.

See salaries and explore the career path for IT Security Specialist

Database Administrator

A database administrator manages and maintains databases. This role may find some value in understanding how data is used in local LLMs. This course, focusing on local LLMs, goes in depth on data privacy, which is a concern for administrators. The course goes over different types of models that can exist, and that a database administrator may be in charge of storing. The course also focuses on Python and building applications with these models, which may be useful for an administrator to understand how to interact with this data. The administrator may also find it useful to learn how to develop RAG applications as it relates to data organization. As data is stored and retrieved from databases, a database administrator will find information in this course that they can apply to the organization of their own databases. This course may help administrators who are managing databases that use LLMs.

See salaries and explore the career path for Database Administrator

Technical Writer

A technical writer creates documentation for software, systems, and technology products. This course may offer some value for a technical writer who needs to understand and document new technologies. While this course doesn't directly teach technical writing the technical writer will see value in being able to test and experiment with technologies to gain real-world experience. This course focuses on using Python to build applications using Ollama, which the technical writer can document for the user. This course discusses the setup, customization, and integration of different models, which is important for documentation. The hands-on approach of the course can help a technical writer gain a practical understanding of the technologies. This may provide some benefit to technical writers who want a deeper understanding of AI and its related technologies.

See salaries and explore the career path for Technical Writer

Mastering Ollama

Build Private Local LLM Apps with Python

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