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Full-Stack AI with Ollama
Llama, Deepseek, Mistral, QwQ
School of AI
4.5
Based on 168 ratings
School of AI
Full-Stack AI with Ollama: Llama, DeepSeek, Mistral, QwQ, Phi-2, MedLlama2, Granite3.2 is the ultimate hands-on AI development course that teaches you how to build and deploy real-world AI applications using the latest open-source AI models. Whether you're a beginner exploring artificial intelligence or an experienced developer, this course will provide you with practical projects to integrate large language models (LLMs) into web applications, automation tools, and advanced AI-driven solutions.
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Full-Stack AI with Ollama: Llama, DeepSeek, Mistral, QwQ, Phi-2, MedLlama2, Granite3.2 is the ultimate hands-on AI development course that teaches you how to build and deploy real-world AI applications using the latest open-source AI models. Whether you're a beginner exploring artificial intelligence or an experienced developer, this course will provide you with practical projects to integrate large language models (LLMs) into web applications, automation tools, and advanced AI-driven solutions.
Read more
Full-Stack AI with Ollama: Llama, DeepSeek, Mistral, QwQ, Phi-2, MedLlama2, Granite3.2 is the ultimate hands-on AI development course that teaches you how to build and deploy real-world AI applications using the latest open-source AI models. Whether you're a beginner exploring artificial intelligence or an experienced developer, this course will provide you with practical projects to integrate large language models (LLMs) into web applications, automation tools, and advanced AI-driven solutions.
Throughout this course, you will learn how to install, configure, and use Ollama to run powerful AI models locally without relying on expensive cloud-based APIs. You’ll work with LLaMA 3, DeepSeek, Mistral, Mixtral, QwQ, Phi-2, MedLlama2, Granite3.2 and CodeLlama, gaining expertise in natural language processing (NLP), text generation, code completion, debugging, document analysis, sentiment analysis, and AI-driven automation.
The course is packed with real-world AI projects. You will develop an AI news summarizer, create an AI-powered proofreading tool, build a customer support chatbot, and implement an intelligent assistant for business automation. Each project provides hands-on experience with FastAPI, Python, Ollama, and REST APIs, ensuring you gain full-stack development skills in AI integration.
This course also teaches you how to fetch and process real-time data using APIs, making it ideal for those looking to build AI-driven applications that analyze real-time information. You’ll create a real-time news summarizer, an AI-powered financial report analyzer, and an AI job application screener to automate recruiting.
By the end of this course, you will have built AI-powered projects, covering full-stack AI development, text processing, natural language understanding, chatbot development, AI automation, and LLM-based applications. You will be confident in deploying AI models, integrating them into production-ready applications, and leveraging state-of-the-art AI technologies to build intelligent solutions.
Whether you are a developer, data scientist, entrepreneur, researcher, or AI enthusiast, this course will provide you with the skills to implement AI models effectively. You will gain hands-on expertise in building AI-powered web applications, integrating NLP models, and automating tasks with AI-driven tools. This course is perfect for those who want to bridge the gap between AI research and practical implementation by working with top-performing models from Ollama.
If you are ready to take your AI development skills to the next level and build cutting-edge AI-powered applications, then this is the perfect course for you.
Register for this course and see more details by visiting:
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What's inside
Learning objectives
- Understand ai model deployment – learn how to install, set up, and run ai models locally using ollama.
- Build ai-powered applications: develop real-world ai applications using top models from ollama, including llama 3, mistral, codellama, mixtral, and deepseek-r1.
- Implement nlp tasks – work with ai models to summarize text, generate content, proofread documents, and extract key information from legal and business texts.
- Develop ai-powered assistants – build ai chatbots, customer support bots, and personal ai assistants using advanced llms.
- Generate & debug code with ai – utilize codellama to auto-generate code, debug programming errors, and improve software development efficiency.
- Integrate ai with web apps – learn how to create full-stack applications with a fastapi backend and interactive web ui, using ai models for real-time processing
- Automate business & productivity tasks – implement ai solutions for automated email replies, ai-powered meeting summarization, and resume generation.
- Work with real-world data & apis – fetch live data from news apis, finance apis, and customer reviews, and analyze them using ai models for insights.
- Optimize ai model performance – learn techniques for fine-tuning ai prompts, handling api responses, and improving response accuracy.
- Understand ai model deployment – learn how to install, set up, and run ai models locally using ollama.
- Build ai-powered applications: develop real-world ai applications using top models from ollama, including llama 3, mistral, codellama, mixtral, and deepseek-r1.
- Implement nlp tasks – work with ai models to summarize text, generate content, proofread documents, and extract key information from legal and business texts.
- Develop ai-powered assistants – build ai chatbots, customer support bots, and personal ai assistants using advanced llms.
- Generate & debug code with ai – utilize codellama to auto-generate code, debug programming errors, and improve software development efficiency.
- Integrate ai with web apps – learn how to create full-stack applications with a fastapi backend and interactive web ui, using ai models for real-time processing
- Automate business & productivity tasks – implement ai solutions for automated email replies, ai-powered meeting summarization, and resume generation.
- Work with real-world data & apis – fetch live data from news apis, finance apis, and customer reviews, and analyze them using ai models for insights.
- Optimize ai model performance – learn techniques for fine-tuning ai prompts, handling api responses, and improving response accuracy.
Syllabus
Quick Start - Setting Up Ollama & First AI Model
Introduction to Section: Quick Start - Setting Up Ollama & First AI Model
Introduction to Ollama
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Syllabus
Quick Start - Setting Up Ollama & First AI Model
Introduction to Section: Quick Start - Setting Up Ollama & First AI Model
Introduction to Ollama
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Career center
Learners who complete Full-Stack AI with Ollama: Llama, Deepseek, Mistral, QwQ will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Developer
Artificial Intelligence Developer
An Artificial Intelligence Developer is at the forefront of creating intelligent systems and applications across various domains. This course offers an unparalleled pathway for individuals aiming to become a proficient Artificial Intelligence Developer, focusing on building and deploying real-world AI solutions. By mastering Ollama for local execution of models like LLaMA 3, DeepSeek, and CodeLlama, learners gain expertise in full-stack AI development, natural language processing, and AI automation. The curriculum’s emphasis on projects such as an AI-powered proofreading tool and a financial report analyzer provides practical skills in integrating AI into web applications and automating complex tasks, making it ideal for those eager to contribute to cutting-edge AI innovation.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, deploys, and maintains AI models that drive intelligent applications. This course empowers aspiring Machine Learning Engineers by providing comprehensive, hands-on experience with installing, configuring, and running powerful AI models locally using Ollama. Learners will develop full-stack AI applications, integrating models like LLaMA 3 and Mistral into production-ready solutions using Python and FastAPI. The practical projects, including an AI news summarizer and customer support chatbot, directly translate into the skills required to design and implement robust AI systems, covering deployment, natural language processing, and performance optimization critical for success in this demanding field.
Full Stack AI Application Developer
Full Stack AI Application Developer
A Full Stack AI Application Developer combines expertise in both front-end and back-end development with the capability to integrate artificial intelligence functionalities. This course is explicitly designed to cultivate the skills required for a Full Stack AI Application Developer. Learners master creating full-stack applications with a FastAPI backend and interactive web UI, seamlessly integrating AI models for real-time processing. Practical projects, such as building AI-powered web applications and customer support chatbots, provide hands-on experience in using Python, FastAPI, and REST APIs to deploy AI models. This curriculum ensures learners can confidently build and integrate AI-driven solutions from end-to-end, bridging the gap between AI models and accessible user interfaces.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer specializes in enabling computers to understand, interpret, and generate human language. This course is exceptionally well-suited for a Natural Language Processing Engineer, offering deep dives into practical NLP tasks. Learners work with advanced AI models like Mistral and Phi-2 to summarize text, generate content, proofread documents, and extract key information from legal and business texts. The experience with real-time news summarizers, AI blog writers, and legal document analyzers provides invaluable hands-on expertise in natural language understanding and generation. The course’s focus on integrating these powerful LLMs into applications helps build a strong foundation for developing sophisticated language-centric AI solutions.
AI Integration Specialist
AI Integration Specialist
An AI Integration Specialist focuses on seamlessly incorporating artificial intelligence capabilities into existing systems and workflows. This course equips learners with extensive practical expertise ideal for an AI Integration Specialist. It covers how to install, configure, and use Ollama to run powerful AI models locally and integrate them into web applications using FastAPI, Python, and REST APIs. Projects like the customer support chatbot, AI-powered virtual assistant, and real-time news summarizer provide hands-on experience in connecting AI models with diverse data sources and operational systems. This comprehensive training in full-stack AI development ensures confidence in deploying AI models and leveraging state-of-the-art technologies to build intelligent, integrated solutions.
Software Engineer (Machine Learning)
Software Engineer (Machine Learning)
A Software Engineer Machine Learning is responsible for developing, testing, and maintaining the software infrastructure that supports machine learning models and applications. This course offers highly practical skills essential for a Software Engineer Machine Learning. Learners gain hands-on expertise in installing, setting up, and running AI models locally with Ollama, along with developing full-stack AI applications using Python and FastAPI. The syllabus includes specific training on AI code generation and debugging with CodeLlama, directly enhancing software development efficiency. By integrating AI models into web applications and building production-ready solutions, learners develop the robust engineering practices and deep understanding of AI deployment necessary to excel in this specialized field.
Application Developer AI Integration
Application Developer AI Integration
An Application Developer with AI Integration skills focuses on embedding artificial intelligence functionalities into new and existing software applications. This course offers practical, full-stack development experience crucial for an Application Developer specializing in AI integration. Learners master how to create full-stack applications with a FastAPI backend and interactive web UI, leveraging AI models for real-time processing and intelligent features. Projects like building customer support chatbots and AI job application screeners provide direct experience in fetching and processing real-time data using APIs and integrating LLMs effectively. This curriculum ensures confidence in deploying AI models and seamlessly incorporating them into production-ready software, enhancing application intelligence and user experience.
AI Solutions Architect
AI Solutions Architect
An AI Solutions Architect designs and oversees the implementation of complex AI systems, ensuring scalability, integration, and performance. This course provides comprehensive training that aligns closely with the responsibilities of an AI Solutions Architect. Learners gain practical expertise in installing, configuring, and deploying a variety of AI models locally using Ollama, understanding the infrastructure requirements for production-ready applications. The full-stack development skills, API integration, and experience with real-time data processing are critical for designing robust AI solutions. By working on projects that require integrating AI into web applications and automating tasks, learners develop the holistic understanding needed to architect sophisticated AI-powered systems. This role often requires prior experience and may be enhanced by an advanced degree.
Data Scientist with AI Focus
Data Scientist with AI Focus
A Data Scientist with an AI focus delves into complex datasets to uncover insights and build predictive models, often leveraging advanced AI techniques. This course offers highly relevant skills for a Data Scientist, particularly those specializing in AI. Learners gain hands-on experience with natural language processing, document analysis, and sentiment analysis using powerful AI models. The curriculum emphasizes fetching and processing real-time data using APIs and analyzing them with AI models for insights, as seen in projects like the AI-powered financial report analyzer. This practical expertise in deploying and optimizing AI models and integrating them into applications helps build a strong foundation for extracting value from data and developing intelligent data-driven solutions. This role typically requires an advanced degree.
Prompt Engineer
Prompt Engineer
A Prompt Engineer specializes in designing, testing, and refining prompts to optimize the performance and output of large language models. This course directly contributes to the core competencies of a Prompt Engineer by teaching techniques for fine-tuning AI prompts and improving response accuracy. Learners gain hands-on experience working with various powerful AI models like LLaMA 3, Mistral, and DeepSeek, understanding their nuances and how to elicit desired outputs. The practical exposure to text generation, content writing, and debugging using AI models provides a robust framework for crafting effective prompts. This course is ideal for anyone seeking to master the art and science of communicating effectively with AI to achieve optimal results in diverse applications.
AI Automation Engineer
AI Automation Engineer
An AI Automation Engineer focuses on integrating AI capabilities to streamline processes, enhance efficiency, and create intelligent automated workflows. This course provides comprehensive, hands-on skills that are highly relevant for an AI Automation Engineer. Learners are empowered to implement AI solutions for automated email replies, meeting summarization, and resume generation, alongside building an intelligent assistant for business automation. The practical experience with deploying AI models, integrating them into applications, and utilizing open-source tools like Ollama and FastAPI helps build a foundation for designing and implementing sophisticated AI-driven automation systems. This course is ideal for professionals looking to leverage AI to transform operational efficiency and productivity across various industries.
AI Product Manager
AI Product Manager
An AI Product Manager guides the development and strategy of AI-driven products, requiring a deep understanding of AI capabilities and implementation. This course offers a robust technical foundation that may be useful for an aspiring AI Product Manager. While not a product management course, it provides invaluable insights into the practicalities of building and deploying AI applications, understanding model limitations, and leveraging open-source tools like Ollama. Projects like the AI news summarizer, customer support chatbot, and AI job application screener demonstrate real-world AI use cases, helping learners grasp the feasibility and complexity of AI-driven solutions. This technical understanding is crucial for communicating effectively with engineering teams and making informed product decisions; this role often benefits from an advanced degree.
Applied AI Researcher
Applied AI Researcher
An Applied AI Researcher investigates how cutting-edge artificial intelligence models can be practically implemented to solve real-world problems. This course offers directly relevant experience for an Applied AI Researcher. Learners gain hands-on expertise in working with top-performing open-source AI models, including LLaMA 3, Mistral, and DeepSeek, installing and configuring them locally using Ollama. The curriculum’s emphasis on developing real-world AI applications, such as an AI news summarizer or a medical AI symptom checker, helps build a foundation in translating theoretical AI advancements into tangible solutions. This practical experience in model deployment, natural language processing, and integrating AI into functional applications is invaluable for exploring and validating new AI use cases effectively; this role typically requires an advanced degree.
AI Business Analyst
AI Business Analyst
An AI Business Analyst identifies business needs and translates them into AI-driven solutions, bridging the gap between business strategy and technical implementation. This course offers highly relevant insights that may be useful for an aspiring AI Business Analyst. Learners gain practical understanding of how AI models like LLaMA 3 and Mistral can be deployed and integrated to automate tasks and enhance productivity. The experience with real-world projects such as an AI-powered financial report analyzer, a customer support chatbot, and an AI job application screener directly demonstrates the business value and feasibility of AI solutions. This hands-on exposure to full-stack AI development and practical application helps build a foundation for evaluating AI opportunities and collaborating effectively with technical teams.
Quantitative Researcher AI
Quantitative Researcher AI
A Quantitative Researcher AI applies advanced mathematical, statistical, and computational methods, often using AI, to analyze financial markets or complex systems. This course may be useful for an aspiring Quantitative Researcher AI by providing practical experience in leveraging AI for data analysis, particularly with the AI-powered financial report analyzer project. Learners gain hands-on expertise in fetching and processing real-time data using APIs and analyzing them with AI models, which is crucial for uncovering insights in quantitative fields. While the course focuses on implementation rather than theoretical quantitative methods, the ability to deploy and utilize state-of-the-art AI technologies for data extraction and analysis can help build a strong foundation for applied research. This role typically requires an advanced degree.
For more career information including salaries, visit:
OpenCourser.com/course/rn0y6z/full
Reading list
We haven't picked any books for this reading list yet.
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Explores the potential impact of LLMs on the future of AI and society. It discusses the ethical implications of LLMs and the challenges that need to be addressed.
Provides a detailed overview of language models, including LLMs. It focuses on the theoretical foundations of language models and their applications in NLP.
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Provides a comprehensive overview of deep learning, including LLMs. It valuable resource for anyone who wants to learn more about the theoretical foundations of LLMs.
This classic textbook covers a wide range of topics in speech and language processing, including LLMs. It provides a comprehensive overview of the field and valuable resource for anyone who wants to learn more about LLMs.
Retrieval Augmented Generation (RAG) crucial technique for providing LLMs with up-to-date and domain-specific information, a common need when using local models via Ollama. delves into building RAG pipelines, making it highly relevant for enhancing LLM applications. This book is valuable for understanding and implementing RAG.
Another classic and comprehensive textbook covering a wide range of topics in NLP and computational linguistics. Similar to Manning and Schütze, it provides foundational knowledge essential for a thorough understanding of the field that LLMs belong to. This widely used textbook in academic settings.
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Offers an accessible overview of generative AI, explaining the core ideas without excessive technical jargon. It is suitable for gaining a broad understanding of the field that Ollama operates within. It serves as helpful background reading for those new to generative AI.
Focusing on the LangChain framework, this book is highly relevant for building applications with LLMs, a key theme in the provided course names. It covers practical aspects of using LLMs and frameworks like LangChain, which are often used in conjunction with local LLMs served by Ollama. is valuable for hands-on application development.
Provides a hands-on approach to building applications with LLMs, including the creation of intelligent agents. It covers practical aspects and frameworks like LangChain, which are directly applicable to developing applications that utilize local LLMs via Ollama.
Focuses on key techniques like RAG and fine-tuning, which are directly applicable to enhancing the performance and relevance of LLMs run with Ollama. It provides practical guidance for improving the capabilities of local models for specific tasks. This book is highly relevant for contemporary LLM application development.
Is excellent for gaining a deep, foundational understanding of how LLMs work by guiding you through building one from scratch using Python and PyTorch. It covers the core concepts and is highly valuable for solidifying understanding, serving as a strong prerequisite for working with tools like Ollama. This book practical guide rather than a theoretical reference. It is well-regarded in the field and is suitable for those with intermediate Python and some machine learning knowledge.
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Given the course names mentioning AI agents, this book is highly relevant. It focuses on building intelligent agents powered by LLMs, covering frameworks and techniques for creating autonomous systems. This aligns with the advanced applications of LLMs that can be explored using Ollama.
Prompt engineering crucial skill for effectively using LLMs. focuses on the principles and techniques for designing prompts to get reliable outputs from generative AI models. This is directly applicable to interacting with and getting the best results from LLMs run locally with Ollama.
While broader than just LLMs, this book covers the essential principles of designing and deploying machine learning systems, including aspects of MLOps relevant to putting LLMs into production environments. It provides a solid understanding of the system-level considerations. is valuable for understanding the broader context of deploying AI systems.
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Introduces the fundamental concepts of MLOps, providing a framework for understanding the lifecycle of machine learning models in production. While not solely focused on LLMs, the principles discussed are directly applicable to deploying and managing LLMs with Ollama. It good starting point for understanding MLOps.
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Focuses on the practical aspects of MLOps, which are highly relevant for deploying and managing LLMs with a tool like Ollama. It covers topics like monitoring, deployment, and operationalization. It provides hands-on guidance for putting models into practice.
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This foundational text in the field of deep learning, providing the theoretical and mathematical background necessary to understand the internal workings of LLMs. While not specific to Ollama, it offers essential prerequisite knowledge for a deep understanding of the models. It is widely considered a classic textbook in deep learning.
This concise book offers a hands-on introduction to language models and transformers using PyTorch. It provides a solid technical overview without being overly lengthy, making it a good resource for quickly grasping the core concepts behind LLMs that can be used with Ollama.
Transformers are the architecture behind most modern LLMs. provides a deep dive into transformers and using the Hugging Face library, a popular tool for working with these models. While not directly about Ollama, it's highly relevant for understanding and potentially customizing models used with Ollama.
Provides a broad introduction to the concepts and techniques behind generative AI, including the models that Ollama can run. It's a good starting point for understanding the 'what' and 'how' of generative models before diving into specific tools like Ollama. It is valuable as foundational reading.
For more information about how these books relate to this course, visit:
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