Mastering Local LLMs with Ollama and Python + Doing Projects
Mastering Local LLMs with Ollama and Python + Doing Projects, available at $54.99, has an average rating of 4.92, with 13 lectures, based on 6 reviews, and has 49 subscribers.
You will learn about 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 This course is ideal for individuals who are Python developers interested in working with local LLM models or AI enthusiasts looking to explore Ollama and its integration with Python or Data scientists wanting to incorporate LLMs into their workflows or Software engineers aiming to build AI-powered applications or Students studying artificial intelligence or natural language processing or Professionals seeking to automate tasks using LLM models It is particularly useful for Python developers interested in working with local LLM models or AI enthusiasts looking to explore Ollama and its integration with Python or Data scientists wanting to incorporate LLMs into their workflows or Software engineers aiming to build AI-powered applications or Students studying artificial intelligence or natural language processing or Professionals seeking to automate tasks using LLM models.
Enroll now: Mastering Local LLMs with Ollama and Python + Doing Projects
Summary
Title: Mastering Local LLMs with Ollama and Python + Doing Projects
Price: $54.99
Average Rating: 4.92
Number of Lectures: 13
Number of Published Lectures: 13
Number of Curriculum Items: 13
Number of Published Curriculum Objects: 13
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- 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
Who Should Attend
- 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
Target Audiences
- 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
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 tools
This 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.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Chapter 2: Course Content
Lecture 1: 1 Download and Use OLLAMA
Lecture 2: 2 Use Ollama with Python
Lecture 3: 3 Use Streamlit
Lecture 4: 4 Integrate LLM Model to Streamlit
Lecture 5: 5 Use Exac function to run a Code in String Type
Lecture 6: 6 Get the output of the Exac function as a variable
Lecture 7: 7 Do a Learning Python Tool with Ollama
Lecture 8: 8 Make llava describe the image
Lecture 9: 9 Make a Image Describer page on Streamlit
Lecture 10: 10 Describe multiple images
Lecture 11: 11 Separate a video into frames
Lecture 12: 12 Make a Video Describer
Instructors
-
Abdurrahman TEKIN
PhD student
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 1 votes
- 5 stars: 5 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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