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AI Development with Qwen 2.5 & Ollama

Build AI Apps Locally

School of AI

4.1

Based on 103 ratings

School of AI

Are you ready to build AI-powered applications locally without relying on cloud-based APIs? This hands-on course will teach you how to develop, optimize, and deploy AI applications using Qwen 2.5 and Ollama, two powerful tools for running large language models (LLMs) on your local machine.

With the rise of open-source AI models, developers now have the opportunity to create intelligent applications that process text, generate content, and automate tasks—all while keeping data private and secure. In this course, you’ll learn how to install, configure, and integrate Qwen 2.5 with Ollama, build FastAPI-based AI backends, and develop real-world AI solutions.

Why Learn Qwen 2.5 and Ollama?

Qwen 2.5 is a powerful large language model (LLM) developed by Alibaba Cloud, optimized for natural language processing (NLP), text generation, reasoning, and code assistance. Unlike traditional cloud-based models like GPT-4, Qwen 2.5 can run locally, making it ideal for privacy-sensitive AI applications.

Ollama is an AI model management tool that allows developers to run and deploy LLMs locally with high efficiency and low latency. With Ollama, you can pull models, run them in your applications, and fine-tune them for specific tasks—all without the need for expensive cloud resources.

This course is practical and hands-on, designed to help you apply AI in real-world projects. Whether you want to build AI-powered chat interfaces, document summarizers, code assistants, or intelligent automation tools, this course will equip you with the necessary skills.

Why Take This Course?

- Hands-on AI development with real-world projects- No reliance on cloud APIs—keep your AI applications private & secure- Future-proof skills for working with open-source LLMs- Fast, efficient AI deployment with Ollama’s local execution

By the end of this course, you'll have AI-powered applications running on your machine, a deep understanding of LLMs, and the skills to develop future AI solutions. Are you ready to start building?

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What's inside

Learning objectives

Set up and run qwen 2.5 on a local machine using ollama
Understand how large language models (llms) work
Build ai-powered applications using python and fastapi
Create rest apis to interact with ai models locally
Integrate ai models into web apps using react.js

Optimize and fine-tune ai models for better performance
Implement local ai solutions without cloud dependencies
Use ollama cli and python sdk to manage ai models
Deploy ai applications locally and on cloud platforms
Explore real-world ai use cases beyond chatbots

Set up and run qwen 2.5 on a local machine using ollama
Understand how large language models (llms) work
Build ai-powered applications using python and fastapi
Create rest apis to interact with ai models locally
Integrate ai models into web apps using react.js
Optimize and fine-tune ai models for better performance
Implement local ai solutions without cloud dependencies
Use ollama cli and python sdk to manage ai models
Deploy ai applications locally and on cloud platforms
Explore real-world ai use cases beyond chatbots

Syllabus

Deep Dive intoQwen 2.5

What is Qwen 2.5?

Qwen 2.5 vs. Other Models - Llama3, GPT-4, Mistral

What is Ollama?

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Career center

Learners who complete AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally will develop knowledge and skills that may be useful to these careers:

Artificial Intelligence Developer

An Artificial Intelligence Developer focuses on creating intelligent applications that leverage AI models to perform complex tasks. This involves understanding AI principles, selecting appropriate models, and implementing solutions across various platforms. This course is specifically tailored to equip learners with the skills to become an Artificial Intelligence Developer. It teaches how to develop, optimize, and deploy AI applications using powerful tools like Qwen 2.5 and Ollama for running large language models locally. Learners build FastAPI-based AI backends and React frontends, gaining experience in creating real-world AI solutions such as chatbots, document summarizers, and code assistants. This emphasis on hands-on development and understanding LLMs is key for an aspiring developer in this dynamic field.

See salaries and explore the career path for Artificial Intelligence Developer

Natural Language Processing Engineer

A Natural Language Processing Engineer specializes in developing systems that enable computers to understand, interpret, and generate human language. This can involve tasks like sentiment analysis, text summarization, machine translation, and chatbot development. This course is highly relevant for an aspiring Natural Language Processing Engineer. It provides a deep understanding of large language models (LLMs) like Qwen 2.5, which are optimized for natural language processing, text generation, and reasoning. Learners acquire hands-on experience in building AI-powered chat interfaces and document summarizers, directly applying NLP concepts. The focus on running and fine-tuning LLMs locally through Ollama equips individuals with practical skills crucial for developing advanced NLP solutions.

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Machine Learning Engineer

A Machine Learning Engineer is crucial for designing, building, and deploying machine learning models and systems. This role involves everything from data preparation and model training to integrating models into production environments. The course, "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally," provides direct, practical experience vital for this career. Learners acquire skills in setting up and running large language models like Qwen 2.5 using Ollama, building AI-powered applications with Python and FastAPI, and creating REST APIs to interact with these models locally. This hands-on approach to implementing local AI solutions and optimizing models for performance is precisely what a Machine Learning Engineer needs to successfully deploy future AI solutions, whether locally or on cloud platforms.

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Full-Stack Developer

A Full Stack Developer possesses expertise in both frontend and backend development, enabling them to build complete web applications from user interface to server-side logic and database management. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course provides highly relevant skills for a Full Stack Developer. It teaches how to build AI-powered applications, specifically by developing FastAPI-based AI backends and integrating AI models into web apps using React.js for the frontend. This holistic approach means learners gain experience in creating REST APIs, understanding environment setup, and deploying complete AI solutions. Those aspiring to become a Full Stack Developer with a specialization in integrating modern AI capabilities will find this course helps build a comprehensive skill set.

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Application Developer

An Application Developer focuses on building software applications for various platforms, including web, mobile, or desktop, to meet specific user needs. This role involves coding, testing, and debugging. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course is highly relevant for an Application Developer looking to incorporate cutting-edge AI capabilities into their projects. The course teaches how to build AI-powered applications using Python and FastAPI for the backend and React.js for the frontend. Learners gain practical experience in creating REST APIs, integrating AI models into web apps, and deploying these solutions. This specialized training in local, private, and secure AI application development equips an Application Developer with future-proof skills for creating intelligent, performant, and data-private applications.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications and systems across various platforms. This foundational role requires strong programming skills and an understanding of software development life cycles. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course is highly pertinent for a Software Engineer looking to specialize in artificial intelligence. It emphasizes hands-on AI development with Python and FastAPI, building AI-powered applications, and creating REST APIs. Learners also gain experience integrating AI models into web apps using React.js. This course helps build practical skills in developing and deploying secure, private AI solutions, enhancing a Software Engineer's ability to tackle complex, intelligent application development challenges in a privacy-conscious world.

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Artificial Intelligence Prompt Engineer

An Artificial Intelligence Prompt Engineer specializes in crafting, refining, and optimizing prompts for large language models to elicit desired outputs and achieve specific application goals. This role requires a deep understanding of how LLMs work and their capabilities. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course provides an excellent foundation for an Artificial Intelligence Prompt Engineer. It offers a deep understanding of large language models (LLMs), including Qwen 2.5 and comparisons to other models like GPT-4. By building AI-powered applications, such as chatbots and document summarizers, learners gain insights into how LLMs respond to inputs and how to integrate them effectively. This practical experience with LLMs and their application is crucial for developing sophisticated prompting strategies and ensuring optimal model performance in real-world scenarios.

See salaries and explore the career path for Artificial Intelligence Prompt Engineer

Edge Artificial Intelligence Engineer

An Edge Artificial Intelligence Engineer designs and implements AI solutions that run directly on local devices or edge infrastructure, minimizing reliance on cloud services and prioritizing privacy. This role is crucial for deploying intelligent capabilities closer to the data source. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course provides highly relevant experience for an Edge Artificial Intelligence Engineer. It focuses intensely on building and deploying AI-powered applications locally using Qwen 2.5 and Ollama, specifically highlighting the benefits of privacy-sensitive AI applications without cloud API reliance. Learners gain skills in optimizing and fine-tuning AI models for better performance on local machines, which is essential for resource-constrained edge environments, ensuring fast and efficient local execution for real-world AI solutions.

See salaries and explore the career path for Edge Artificial Intelligence Engineer

Backend Developer

A Backend Developer is responsible for the server side of web applications, focusing on databases, APIs, and server logic to ensure seamless data flow and functionality. This role often involves building robust and scalable systems. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course may be useful for a Backend Developer looking to specialize in AI. It focuses on building FastAPI-based AI backends and creating REST APIs to interact with AI models locally. By learning Python and FastAPI within the context of AI application development, learners gain practical experience in core backend technologies. This course helps build a foundation in integrating complex AI functionalities into backend services, a skill increasingly valuable as AI permeates all aspects of software development.

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Artificial Intelligence Solutions Architect

An Artificial Intelligence Solutions Architect designs and oversees the implementation of AI systems within an organization, ensuring they align with business objectives and technical requirements. This strategic role involves understanding various AI technologies and how to integrate them effectively. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course may be useful for an aspiring Artificial Intelligence Solutions Architect. It covers understanding large language models, building real-world AI solutions, and deploying AI applications, locally and on cloud platforms. While an Artificial Intelligence Solutions Architect typically requires an advanced degree and extensive industry experience, this course helps build a practical understanding of open-source LLMs like Qwen 2.5 and efficient local deployment with Ollama, enabling informed architectural decisions.

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Technical Lead Software Engineering

A Technical Lead Software Engineering guides software development teams, making architectural decisions, mentoring engineers, and ensuring the technical quality and successful delivery of projects. This role requires deep technical expertise and leadership skills. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course may be useful for a Technical Lead Software Engineering aiming to lead teams in AI-driven projects. It helps build a practical understanding of developing, optimizing, and deploying AI applications using open-source large language models like Qwen 2.5 and tools like Ollama. This experience with building FastAPI-based AI backends and React frontends, coupled with knowledge of local and cloud deployments, can enable a Technical Lead to effectively guide technical strategy and implementation for AI-powered solutions, ensuring data privacy and security.

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Research Scientist, Artificial Intelligence

A Research Scientist Artificial Intelligence explores new AI algorithms, models, and techniques, often pushing the boundaries of what's possible in the field. This role typically involves deep theoretical knowledge, experimentation, and publication. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course may be useful for an aspiring Research Scientist Artificial Intelligence. It provides a deep dive into large language models such as Qwen 2.5 and compares them with other cutting-edge models like Llama3 and GPT-4. Learners gain hands-on experience in understanding how LLMs work, optimizing and fine-tuning AI models, and implementing local AI solutions. While a Research Scientist Artificial Intelligence typically requires an advanced degree, often a PhD, this course helps build a practical foundation in contemporary LLM technologies and their local deployment, informing experimental design and applied research.

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Artificial Intelligence Quality Assurance Engineer

An Artificial Intelligence Quality Assurance Engineer focuses on testing and validating AI systems to ensure their reliability, accuracy, fairness, and performance. This involves designing test cases and evaluating model behaviors. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course may be useful for an Artificial Intelligence Quality Assurance Engineer. It helps build a practical understanding of how large language models (LLMs) work, how they are built into applications using Python and FastAPI, and how they are optimized and fine-tuned for better performance. By understanding the internals of AI application development, including local deployment with Qwen 2.5 and Ollama, an Artificial Intelligence Quality Assurance Engineer can more effectively design comprehensive testing strategies, identify potential biases or failures, and ensure the quality of intelligent automation tools and other AI-powered solutions.

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Data Scientist

A Data Scientist analyzes complex datasets to extract insights, build predictive models, and inform strategic decisions. This role often involves statistical analysis, machine learning, and data visualization. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course may be useful for a Data Scientist, particularly one focusing on natural language processing or MLOps aspects of their role. While the course is heavily focused on application development rather than core data analysis, it helps build an understanding of how large language models (LLMs) work and how to deploy and fine-tune them. This knowledge can be beneficial for Data Scientists who need to integrate LLM-based solutions into their data pipelines or evaluate the performance of AI models in production environments.

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Embedded Systems Engineer

An Embedded Systems Engineer designs and develops software and hardware for specialized computer systems often found within other devices, such as IoT devices or smart appliances. This role requires understanding low-level hardware interaction and resource optimization. The "AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally" course may be useful for an Embedded Systems Engineer interested in integrating AI capabilities into edge devices. The course emphasizes running large language models like Qwen 2.5 locally with high efficiency and low latency using Ollama, without relying on cloud APIs. This focus on local execution, optimization, and resource management for AI applications could be particularly relevant for developing intelligent features on constrained embedded platforms, enabling private and secure AI processing directly on the device.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AI Development with Qwen 2.5 & Ollama: Build AI Apps Locally.

Artificial Intelligence

Save

The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.

Artificial Intelligence: A Modern Approach

Hardcover

$$$$

Fluent Python

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Serves as the ultimate masterclass in idiomatic Python, guiding developers through data models, generators, metaprogramming, and concurrent concurrency patterns.

AI Development with Qwen 2.5 & Ollama

What's inside

Learning objectives

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Career center

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