Mastering Local LLMs with Ollama and Python + Doing Projects

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Abdurrahman TEKIN

1:02:31

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1. Introduction

1. Introduction.mp4

04:08

2. Course Content

1. 1 Download and Use OLLAMA.mp4

07:11

2. 2 Use Ollama with Python.mp4

07:05

3. 3 Use Streamlit.mp4

05:53

4. 4 Integrate LLM Model to Streamlit.mp4

06:37

5. 5 Use Exac function to run a Code in String Type.mp4

05:41

6. 6 Get the output of the Exac function as a variable.mp4

04:25

7. 7 Do a Learning Python Tool with Ollama.mp4

21:31

Files.zip

Description

Mastering Local LLMs with Ollama and Python: From Installation to Advanced Applications and AI-Powered Learning Tools

What You'll 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 is this for?

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

What You Need to Know?

Basic Knowledge of Python

More details

Description
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.

Who this course is for:
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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Python

Abdurrahman TEKIN

Instructor's Courses

My name is Abdurrahman Tekin and I am a Ph.D. student in the field of aircraft design. In addition to my research, I also have extensive experience in teaching. I have been teaching for the last 4 years and have had the privilege of working with students from 161 different countries.As a teacher, I specialize in teaching Chinese, English, and Python. I have a passion for these languages and enjoy sharing my knowledge with my students. With my expertise, I am confident that I can help you improve your language skills and achieve your goals.In addition to my teaching experience, I also have a strong background in research. As a Ph.D. student in aircraft design, I have a deep understanding of the latest techniques and technologies in the field. This allows me to provide my students with valuable insights and practical knowledge that can be applied in real-world situations.Overall, I am committed to providing a high-quality learning experience for my students. I am dedicated to helping you achieve your goals, and I am always available to answer any questions you may have. Whether you are a beginner or an advanced learner, I am confident that I can help you improve your skills and reach your full potential.

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