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Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor
Ready to harness the power of local AI development with DeepSeek R1 and Ollama?
In this comprehensive masterclass, you'll learn how to build sophisticated AI applications locally using DeepSeek R1, one of the most powerful open-source language models available today.
Whether you're a developer looking to integrate AI into your applications or an AI enthusiast wanting to build custom solutions, this course provides everything you need to succeed.
Why This Course?
✓ Learn to run AI models locally for complete privacy and control
✓ Save costs by avoiding expensive API calls and cloud services
Read more
Ready to harness the power of local AI development with DeepSeek R1 and Ollama?
In this comprehensive masterclass, you'll learn how to build sophisticated AI applications locally using DeepSeek R1, one of the most powerful open-source language models available today.
Whether you're a developer looking to integrate AI into your applications or an AI enthusiast wanting to build custom solutions, this course provides everything you need to succeed.
Why This Course?
✓ Learn to run AI models locally for complete privacy and control
✓ Save costs by avoiding expensive API calls and cloud services
Read more
Ready to harness the power of local AI development with DeepSeek R1 and Ollama?
In this comprehensive masterclass, you'll learn how to build sophisticated AI applications locally using DeepSeek R1, one of the most powerful open-source language models available today.
Whether you're a developer looking to integrate AI into your applications or an AI enthusiast wanting to build custom solutions, this course provides everything you need to succeed.
Why This Course?
✓ Learn to run AI models locally for complete privacy and control
✓ Save costs by avoiding expensive API calls and cloud services
✓ Build production-ready applications with open-source technologies
✓ Master the latest tools in AI development
✓ Hands-on projects and real-world applications
Course Highlights:
Complete setup guide for DeepSeek R1 with Ollama
Working with different model variants (1.5B, 7B models)
Performance optimization techniques
Building practical AI applications
What Makes This Course Different?
This isn't just another AI course – it's a comprehensive guide to building real-world AI applications using open-source tools. You'll learn not just the theory, but the practical implementation details that make a difference.
Who is this course for?
Software developers wanting to integrate AI into their applications
AI enthusiasts interested in local LLM deployment
Tech professionals looking to build custom AI solutions locally
Anyone interested in open-source AI development
Prerequisites:
Basic Python programming knowledge
Familiarity with command-line operations
Computer with minimum
With lifetime access to course updates, you'll stay current with the latest developments in AI technology.
Enroll now and start building powerful AI applications today.
Money-Back Guarantee This course comes with Udemy's 30-day money-back guarantee.
If you're not completely satisfied, you can request a full refund within 30 days of purchase.
Register for this course and see more details by visiting:
OpenCourser.com/course/0hgcwa/deepseek
What's inside
Learning objectives
- Deploy deepseek r1 locally using ollama to build ai applications with complete privacy, control, and cost efficiency.
- Master model variants (1.5b, 7b, full) and select optimal configurations based on hardware and use case needs.
- Build apps from scratch: chat interfaces, code assistants, and productivity tools using python.
- Optimize ai application performance through resource management and scaling strategies for production use.
- Deploy deepseek r1 locally using ollama to build ai applications with complete privacy, control, and cost efficiency.
- Master model variants (1.5b, 7b, full) and select optimal configurations based on hardware and use case needs.
- Build apps from scratch: chat interfaces, code assistants, and productivity tools using python.
- Optimize ai application performance through resource management and scaling strategies for production use.
Syllabus
Introduction
Introduction & What the Course is About
WATCH THIS: What You'll Build in this Course
Course Prerequisites
Read more
Syllabus
Introduction
Introduction & What the Course is About
WATCH THIS: What You'll Build in this Course
Course Prerequisites
Read more
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Activities
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We're preparing activities for
DeepSeek AI R1 & Ollama Guide: Build Local AI Applications.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete DeepSeek AI R1 & Ollama Guide: Build Local AI Applications will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are at the forefront of designing, building, and deploying intelligent systems. This course directly prepares you for a career as a Machine Learning Engineer by equipping you with the practical skills to deploy sophisticated AI applications locally using DeepSeek R1 and Ollama. You will learn to optimize application performance through resource management and scaling strategies, develop production-ready solutions, and master open-source tools. This specialized training in local, private, and cost-efficient AI development with Python is highly sought-after, making this course an essential step for those aspiring to build and integrate cutting-edge AI.
Artificial Intelligence Developer
Artificial Intelligence Developer
As an Artificial Intelligence Developer, you will be responsible for creating and implementing AI-powered features and applications. This course is specifically designed for you, teaching you how to build practical AI applications from scratch, including chat interfaces and code assistants, using DeepSeek R1 and Ollama. The emphasis on local deployment ensures complete privacy and control over your AI solutions, a critical skill for an Artificial Intelligence Developer. By mastering open-source technologies and hands-on projects, you gain the practical implementation details needed to succeed in this dynamic field.
Deep Learning Engineer
Deep Learning Engineer
A Deep Learning Engineer specializes in designing, training, and deploying neural network models. This course is particularly relevant for a Deep Learning Engineer, providing hands-on expertise in deploying DeepSeek R1, a powerful open-source language model, locally using Ollama. You will learn to work with various model variants, optimize their performance, and integrate them into production-ready AI applications. This deep dive into practical, local LLM deployment with emphasis on specific model architectures and their optimization is ideal for advancing skills in the deep learning domain.
Machine Learning Operations Engineer
Machine Learning Operations Engineer
A Machine Learning Operations Engineer focuses on streamlining the deployment, monitoring, and maintenance of machine learning systems. This course directly addresses core MLOps principles, teaching you to build production-ready applications and master performance optimization techniques for local AI deployment. You will learn resource management and scaling strategies for DeepSeek R1 and Ollama, crucial skills for ensuring stability and efficiency in AI application lifecycles. This practical understanding of local, open-source AI deployment provides a strong foundation for managing operational aspects of intelligent systems effectively.
Applied Machine Learning Scientist
Applied Machine Learning Scientist
An Applied Machine Learning Scientist bridges theoretical research with practical implementation, often prototyping and deploying advanced models. This course can be highly valuable for an Applied Machine Learning Scientist, as it teaches the deployment of powerful open-source language models like DeepSeek R1 locally with Ollama. You will master working with different model variants and optimizing performance, which is crucial for experimenting with and integrating new AI solutions efficiently. This practical, application-focused approach helps build a foundation for bringing cutting-edge AI concepts into real-world use. This role often requires an advanced degree.
Backend Software Engineer
Backend Software Engineer
As a Backend Software Engineer, you build and maintain the server-side logic and APIs that power applications. This course will significantly enhance your capabilities as a Backend Software Engineer by demonstrating how to integrate sophisticated AI functionality into your systems. You will learn to use the Ollama Local API Endpoint to run DeepSeek R1 models in code, enabling you to build powerful AI-driven backend services such as code assistants and document analyzers. This practical experience with local AI deployment and API integration is invaluable for creating modern, intelligent applications.
AI Solutions Architect
AI Solutions Architect
An AI Solutions Architect designs and oversees the implementation of complex AI systems, ensuring they meet business requirements. This course may be useful for an AI Solutions Architect by providing a deep understanding of local AI deployment with open-source tools like DeepSeek R1 and Ollama. You gain insight into the practical implications of privacy, control, and cost efficiency, which are vital considerations when architecting AI solutions. Mastering the ability to deploy and optimize models locally helps build a foundation for making informed technology choices and designing robust, scalable, and secure AI infrastructures. This role often requires an advanced degree.
Research Engineer Artificial Intelligence
Research Engineer Artificial Intelligence
A Research Engineer Artificial Intelligence explores and implements novel AI techniques, often prototyping new models and applications. This course may be useful for a Research Engineer Artificial Intelligence, as it provides hands-on methods for local development and deployment of DeepSeek R1 model variants using Ollama. This practical skill set supports rapid experimentation, performance optimization, and building proofs-of-concept for innovative AI solutions with complete control over the environment. Understanding the practical implementation of open-source LLMs helps bridge the gap between theoretical research and tangible application. This role often requires an advanced degree.
Full-Stack Developer
Full-Stack Developer
A Full Stack Developer builds and maintains both the frontend and backend of web applications. This course may be useful for a Full Stack Developer looking to integrate cutting-edge AI functionalities into their projects. While focusing on the backend AI logic, it directly enables you to build complete AI applications like chat interfaces using DeepSeek R1 and Ollama. Understanding local AI deployment provides significant advantages for creating private, cost-effective, and powerful intelligent features from scratch, enhancing your ability to deliver end-to-end AI-driven solutions.
Technical Consultant Machine Learning
Technical Consultant Machine Learning
A Technical Consultant Machine Learning advises businesses on strategic implementation of AI and ML solutions. This course may be useful for a Technical Consultant Machine Learning by offering practical knowledge of local AI deployment using DeepSeek R1 and Ollama. Understanding the benefits of privacy, control, and cost efficiency inherent in open-source, local solutions is critical for recommending tailored approaches to clients. This hands-on experience in building and optimizing AI applications provides a tangible understanding of capabilities and constraints, enabling more effective and informed client recommendations.
Quantitative Developer
Quantitative Developer
A Quantitative Developer builds and implements complex mathematical and computational models, often in finance. This course may be useful for a Quantitative Developer, as it provides strong skills in deploying and optimizing sophisticated models locally using Python and open-source tools like DeepSeek R1 and Ollama. The focus on performance optimization, resource management, and building robust applications can be directly transferable to developing high-performance analytical tools, simulation engines, or specialized data processing systems in a controlled, private environment. This role often requires an advanced degree.
Technical Product Manager
Technical Product Manager
A Technical Product Manager defines the vision, strategy, and roadmap for technology products. This course may be useful for a Technical Product Manager by providing a practical understanding of building AI applications locally with open-source tools like DeepSeek R1 and Ollama. Insights into local deployment benefits—such as privacy, cost savings, and control—enable more informed product decisions. Grasping the implementation details of AI functionality helps in assessing technical feasibility, prioritizing features, and communicating effectively with engineering teams to guide the development of innovative AI-powered products.
Robotics Software Engineer
Robotics Software Engineer
A Robotics Software Engineer develops the software that controls robotic systems, often integrating advanced intelligence. This course may be useful for a Robotics Software Engineer, especially when developing on-device AI capabilities. The ability to deploy DeepSeek R1 and other AI models locally using Ollama provides a pathway to embed specialized AI functionalities directly onto robotic platforms, ensuring privacy and robust control in environments with limited or no cloud connectivity. Mastering local AI application building fosters innovation in autonomous and intelligent robotics.
Data Engineer
Data Engineer
A Data Engineer designs and builds systems for collecting, processing, and storing data, ensuring it's ready for analysis and machine learning. This course may be useful for a Data Engineer, as understanding the practicalities of local AI application deployment with DeepSeek R1 and Ollama can influence data pipeline design. Knowledge of model variant considerations and performance optimization helps in creating efficient data provisioning strategies for AI systems. This insight into how AI models consume and generate data is beneficial for building robust and scalable data infrastructures to support AI initiatives.
Cybersecurity Analyst Artificial Intelligence
Cybersecurity Analyst Artificial Intelligence
A Cybersecurity Analyst Artificial Intelligence specializes in securing AI systems and data from threats. This course may be useful for a Cybersecurity Analyst Artificial Intelligence due to its strong emphasis on complete privacy and control through local AI deployment using DeepSeek R1 and Ollama. Understanding the practical aspects of building and running AI applications in a controlled, isolated environment directly informs strategies for risk assessment, vulnerability management, and ensuring data integrity and confidentiality within AI infrastructures. This knowledge is crucial for developing secure AI deployments.
For more career information including salaries, visit:
OpenCourser.com/course/0hgcwa/deepseek
Reading list
We haven't picked any books for this reading list yet.
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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.
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.
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.
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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.
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.
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.
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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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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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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.
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.
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.
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.
Focuses specifically on the Transformer architecture, which is the backbone of most modern LLMs. Understanding this architecture is key to a deeper technical understanding of the models that Ollama makes accessible. It valuable resource for those wanting to understand the core technology.
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Discusses the engineering challenges and practices involved in building AI applications with foundation models, including LLMs. It provides valuable insights into the practical aspects of developing and deploying LLM-powered systems, which is relevant for professionals working with Ollama in a production context.
Known for its highly visual approach, this book offers a comprehensive introduction to LLMs, covering their architecture, training, and applications. It's a practical guide that helps solidify understanding through clear explanations and examples. It serves as a good resource for understanding the underlying concepts of the models that Ollama can run.
This foundational textbook in pattern recognition and machine learning, providing essential mathematical and theoretical background relevant to understanding the principles behind many AI models, including LLMs. While not specific to generative AI or LLMs, it offers crucial underlying knowledge. It is considered a classic in the field and is suitable as a textbook or reference.
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:
OpenCourser.com/course/0hgcwa/deepseek
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Rating
4.6
Effort
1.5 total hours
Via
Udemy
Instructor
Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor
Language
English
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