09-10-2024, 04:36 PM
![[Image: b5fed9ca6b9f47a1ba9083f05ce8b33f.jpg]](https://i124.fastpic.org/big/2024/0910/3f/b5fed9ca6b9f47a1ba9083f05ce8b33f.jpg)
Mastering Local Llms With Ollama And Python + Doing Projects
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 923.20 MB | Duration: 1h 3m
Mastering Local LLMs with Ollama and Python: From Installation to Advanced Applications and AI-Powered Learning Tools
[b]What you'll learn[/b]
How to download Ollama from the official website.
Writing Python scripts to interact with LLM models using Ollama
Introduction to Streamlit for creating web applications
Integrate LLM Model to Streamlit
Use exec Function to Run Code in String Type
Get the Output of the exec Function as a Variable
Building an educational tool using Ollama and Streamlit
[b]Requirements[/b]
Basic Knowledge of Python
[b]Description[/b]
This comprehensive course is designed to empower developers, data scientists, and AI enthusiasts with the skills to harness the power of Local Large Language Models (LLMs) using Ollama and Python. Through a hands-on approach, students will learn to seamlessly integrate cutting-edge AI capabilities into their projects without relying on external APIs.Course Overview:Starting with the fundamentals of Ollama, a powerful tool for running LLMs locally, students will progress through a series of practical modules that cover everything from basic setup to advanced application development. The course is structured to provide a perfect balance of theoretical knowledge and practical implementation, ensuring that participants can immediately apply their learning to real-world scenarios.Key Learning Objectives:1. Ollama Fundamentals: Master the installation and usage of Ollama to run LLMs on your local machine, understanding its advantages and capabilities.2. Python Integration: Learn to interface Ollama with Python, enabling programmatic control and customization of LLM interactions.3. Web Application Development: Explore Streamlit to create interactive web applications that leverage the power of local LLMs.4. Advanced LLM Integration: Dive deep into integrating LLM models with Streamlit applications, creating responsive and intelligent user interfaces.5. Dynamic Code Execution: Understand and implement the 'exec' function to run code stored as strings, opening up possibilities for dynamic and flexible applications.6. Output Handling: Master techniques to capture and manipulate the output of dynamically executed code, enhancing the interactivity of your applications.7. Educational Tool Development: Apply all learned concepts to create a Learning Python Tool, demonstrating the practical applications of LLMs in educational technology.By the end of this course, participants will have gained:- Proficiency in using Ollama for local LLM deployment- Skills to integrate LLMs with Python applications- Ability to create interactive web applications using Streamlit- Understanding of dynamic code execution and output handling- Practical experience in developing AI-powered educational toolsThis course is ideal for developers looking to leverage the power of AI without relying on cloud-based services, data scientists aiming to incorporate LLMs into their local workflows, and educators interested in creating advanced, AI-assisted learning tools. With its focus on practical, hands-on learning, participants will leave the course ready to implement sophisticated AI solutions in their projects and organizations.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Course Content
Lecture 2 1 Download and Use OLLAMA
Lecture 3 2 Use Ollama with Python
Lecture 4 3 Use Streamlit
Lecture 5 4 Integrate LLM Model to Streamlit
Lecture 6 5 Use Exac function to run a Code in String Type
Lecture 7 6 Get the output of the Exac function as a variable
Lecture 8 7 Do a Learning Python Tool with Ollama
Python developers interested in working with local LLM models,AI enthusiasts looking to explore Ollama and its integration with Python,Data scientists wanting to incorporate LLMs into their workflows,Software engineers aiming to build AI-powered applications,Students studying artificial intelligence or natural language processing,Professionals seeking to automate tasks using LLM models
![[Image: 5OBc0A6v_o.jpg]](https://images2.imgbox.com/43/ab/5OBc0A6v_o.jpg)
[b]What you'll learn[/b]
How to download Ollama from the official website.
Writing Python scripts to interact with LLM models using Ollama
Introduction to Streamlit for creating web applications
Integrate LLM Model to Streamlit
Use exec Function to Run Code in String Type
Get the Output of the exec Function as a Variable
Building an educational tool using Ollama and Streamlit
[b]Requirements[/b]
Basic Knowledge of Python
[b]Description[/b]
This comprehensive course is designed to empower developers, data scientists, and AI enthusiasts with the skills to harness the power of Local Large Language Models (LLMs) using Ollama and Python. Through a hands-on approach, students will learn to seamlessly integrate cutting-edge AI capabilities into their projects without relying on external APIs.Course Overview:Starting with the fundamentals of Ollama, a powerful tool for running LLMs locally, students will progress through a series of practical modules that cover everything from basic setup to advanced application development. The course is structured to provide a perfect balance of theoretical knowledge and practical implementation, ensuring that participants can immediately apply their learning to real-world scenarios.Key Learning Objectives:1. Ollama Fundamentals: Master the installation and usage of Ollama to run LLMs on your local machine, understanding its advantages and capabilities.2. Python Integration: Learn to interface Ollama with Python, enabling programmatic control and customization of LLM interactions.3. Web Application Development: Explore Streamlit to create interactive web applications that leverage the power of local LLMs.4. Advanced LLM Integration: Dive deep into integrating LLM models with Streamlit applications, creating responsive and intelligent user interfaces.5. Dynamic Code Execution: Understand and implement the 'exec' function to run code stored as strings, opening up possibilities for dynamic and flexible applications.6. Output Handling: Master techniques to capture and manipulate the output of dynamically executed code, enhancing the interactivity of your applications.7. Educational Tool Development: Apply all learned concepts to create a Learning Python Tool, demonstrating the practical applications of LLMs in educational technology.By the end of this course, participants will have gained:- Proficiency in using Ollama for local LLM deployment- Skills to integrate LLMs with Python applications- Ability to create interactive web applications using Streamlit- Understanding of dynamic code execution and output handling- Practical experience in developing AI-powered educational toolsThis course is ideal for developers looking to leverage the power of AI without relying on cloud-based services, data scientists aiming to incorporate LLMs into their local workflows, and educators interested in creating advanced, AI-assisted learning tools. With its focus on practical, hands-on learning, participants will leave the course ready to implement sophisticated AI solutions in their projects and organizations.
Overview
Section 1: Introduction
Lecture 1 Introduction
Section 2: Course Content
Lecture 2 1 Download and Use OLLAMA
Lecture 3 2 Use Ollama with Python
Lecture 4 3 Use Streamlit
Lecture 5 4 Integrate LLM Model to Streamlit
Lecture 6 5 Use Exac function to run a Code in String Type
Lecture 7 6 Get the output of the Exac function as a variable
Lecture 8 7 Do a Learning Python Tool with Ollama
Python developers interested in working with local LLM models,AI enthusiasts looking to explore Ollama and its integration with Python,Data scientists wanting to incorporate LLMs into their workflows,Software engineers aiming to build AI-powered applications,Students studying artificial intelligence or natural language processing,Professionals seeking to automate tasks using LLM models
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