DeepSeek AI for Developers from Udemy

Welcome to this technical deep dive into DeepSeek and their latest GenAI models (DeepSeek-V3 and DeepSeek-R1).

Whether you are an AI enthusiast, a developer, or someone curious about the next big thing in AI, this course will help you understand, use, and even integrate DeepSeek models into your projects.

What You’ll Learn

Introduction to DeepSeek

What is DeepSeek and why does it matter?
A first look at DeepSeek’s models and capabilities
Understanding basic AI terminology

DeepSeek Models & Architecture

DeepSeek-V3 vs. DeepSeek-R1 – Key differences
The open-source aspect & model censorship
Distilled models and how they compare to OpenAI
Image generation with DeepSeek’s Janus model

Using DeepSeek online & offline (locally)

DeepSeek Chat (browser & mobile apps)
Privacy policies & security considerations
Running DeepSeek locally using:
- Ollama
- LM Studio

DeepSeek API

Generating an API key
Making your first API call
API pricing & privacy policy
Running a self-hosted model API

Why Take This Course?

Beginner-friendly – No prior knowledge needed. Step-by-step guidance – From theory to practical applicationsHands-on projects – Learn by doing. Up-to-date content – AI evolves fast, and so does this course. Bonus materials – Extra resources, links, and guides included.

Feeling Overwhelmed? Don’t Worry.

This course is designed to be accessible even if you are new to AI. We will go step by step, and I’m here to answer your questions.

Need help? Use the Q&A section or send me a private message.

Ready to explore DeepSeek? Let’s begin.

What's inside

Learning objectives

Understand the core concepts behind deepseek ai models
Differentiate between deepseek-v3, deepseek-r1, and other models like openai’s offerings.
Use deepseek chat via the browser or mobile app

Run deepseek locally using ollama and lm studio
Make api calls to deepseek platform
Compare deepseek to openai

Understand the core concepts behind deepseek ai models
Differentiate between deepseek-v3, deepseek-r1, and other models like openai’s offerings.
Use deepseek chat via the browser or mobile app
Run deepseek locally using ollama and lm studio
Make api calls to deepseek platform
Compare deepseek to openai

Syllabus

Introduction

What is DeepSeek?

First look at DeepSeek

Overview of the DeepSeek models & capabilities

Let's test your understanding of AI models and other capabilities.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on experience with DeepSeek models, allowing learners to integrate them into projects and gain practical skills in AI application development

Explores the open-source aspect of DeepSeek models, which allows developers to inspect, modify, and distribute the software, fostering innovation and collaboration

Covers running DeepSeek models locally using tools like Ollama and LM Studio, which gives learners greater control over their AI development environment

Examines the differences between DeepSeek-V3 and DeepSeek-R1, which helps learners understand the nuances of different AI architectures and their respective strengths

Discusses privacy policies and security considerations when using DeepSeek, which is crucial for responsible AI development and deployment

Requires learners to set up billing for the DeepSeek platform, which may present a barrier to entry for some learners due to financial constraints

Reviews summary

Practical introduction to deepseek ai models

According to learners, this course offers a good introduction to DeepSeek AI models, covering how to use them via API, Ollama, and LM Studio. Students appreciated the practical examples and demos for integrating DeepSeek into applications, finding it valuable for development tasks. Some found the pace fast, particularly if new to AI or programming, suggesting it might be less beginner-friendly than advertised in certain sections. Overall, it's seen as a solid resource for those looking to work with DeepSeek models and their practical applications.

Kept current with model updates.

"Appreciate that the instructor updates the course as DeepSeek evolves and new models are released."

"The info on DeepSeek-V3 seemed current and relevant to the latest developments."

"Good to see the latest models and their capabilities covered in the materials."

Explores multiple ways to use DeepSeek.

"I learned how to use DeepSeek Chat, Ollama, and LM Studio for accessing the models."

"The section on accessing the models online and offline was very helpful for my needs."

"It clearly explained how to use DeepSeek via API calls and different local options."

Great for developers needing integration.

"The API integration part with Python and LangChain was exactly what I needed for my project ideas."

"Seeing how to use LangChain with DeepSeek was a big plus and very practical."

"The hands-on demos made integrating the API into applications much clearer."

Can be challenging for absolute beginners.

"It says beginner-friendly, but you really need some coding or AI background for the API and integration parts."

"The pace picked up significantly towards the end, requiring quicker grasping of concepts."

"I found the initial parts easy, but the integration examples were tough without prior knowledge."

"While it covers basics, the course quickly moves into technical details that might overwhelm novices."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in DeepSeek AI for Developers with these activities:

Review Basic AI Terminology

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Solidify your understanding of fundamental AI concepts to better grasp the nuances of DeepSeek's models and their applications.

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Review definitions of key terms like neural networks, transformers, and large language models.
Take online quizzes to test your knowledge of AI fundamentals.
Read introductory articles or blog posts on AI to reinforce your understanding.

Read 'Generative AI with Python and TensorFlow 2' by Joseph Babcock

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Gain a broader understanding of generative AI techniques used in models like DeepSeek.

View Generative AI with Python and TensorFlow 2:... on Amazon

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Read the chapters related to generative models and TensorFlow 2.
Experiment with the code examples provided in the book.
Relate the concepts learned to the specific DeepSeek models discussed in the course.

Read 'Hugging Face Transformers' by Lewis Tunstall, Thomas Wolf, and Lysandre Debut

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Gain a deeper understanding of the transformer architecture that powers DeepSeek's models.

View Natural Language Processing with Transformers,... on Amazon

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Read the chapters related to transformer architecture and fine-tuning.
Experiment with the code examples provided in the book.
Relate the concepts learned to the specific DeepSeek models discussed in the course.

Four other activities

Expand to see all activities and additional details

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Experiment with DeepSeek API calls

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Reinforce your understanding of the DeepSeek API by practicing different types of API calls.

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Set up your DeepSeek API key and environment.
Make API calls for chat completion with varying parameters.
Experiment with streaming responses and different message roles.
Analyze the API pricing and usage to optimize your calls.

Build a Simple Chatbot with DeepSeek API

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Apply your knowledge of the DeepSeek API to create a functional chatbot application.

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Design the chatbot's user interface using Gradio or a similar framework.
Implement the API calls to DeepSeek to handle user input and generate responses.
Test and refine the chatbot's functionality and user experience.
Document your chatbot's design and implementation.

Write a Blog Post Comparing DeepSeek and OpenAI

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Solidify your understanding of DeepSeek by comparing it to a well-known alternative like OpenAI.

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Research the key differences between DeepSeek and OpenAI models.
Compare their performance, pricing, and privacy policies.
Write a blog post summarizing your findings.
Publish your blog post on a platform like Medium or your personal website.

Contribute to a DeepSeek Integration Project

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Deepen your understanding of DeepSeek by contributing to an open-source project that integrates with its API.

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Find an open-source project that uses the DeepSeek API.
Identify a bug or feature request to work on.
Submit a pull request with your changes.
Respond to feedback from the project maintainers.

Career center

Learners who complete DeepSeek AI for Developers will develop knowledge and skills that may be useful to these careers:

AI Application Developer

An AI Application Developer builds and maintains applications that leverage artificial intelligence. This course on DeepSeek helps developers learn how to integrate DeepSeek models into their projects. Specifically, the course covers making API calls, creating chat completions, and controlling model behavior. Learning how to integrate DeepSeek with LangChain as well as building user interfaces in Gradio helps the AI Application Developer create AI applications that use the latest AI models. Therefore, a developer who wants to use AI models such as DeepSeek should certainly consider this course.

See salaries and explore the career path for AI Application Developer

Machine Learning Engineer

A Machine Learning Engineer researches, designs, and develops machine learning models to solve complex problems. This DeepSeek course allows them to understand the core concepts behind DeepSeek AI models. Machine Learning Engineers can also learn to run DeepSeek locally using Ollama and LM Studio. The course also covers how to compare DeepSeek to OpenAI, which will be useful to Machine Learning Engineers when deciding which models to use in their projects. For a Machine Learning Engineer who wants to stay at the forefront of AI, this course may be a great choice.

See salaries and explore the career path for Machine Learning Engineer

Prompt Engineer

A Prompt Engineer specializes in crafting effective prompts to elicit desired responses from AI models. The DeepSeek course helps them understand DeepSeek models and their capabilities. By learning how to control model behavior with inference parameters and understanding tokens and message roles, a Prompt Engineer will be better equipped to craft effective prompts for DeepSeek models. They can also learn how to use DeepSeek chat to test prompts and refine them. By taking this course, a Prompt Engineer will become more familiar with DeepSeek and its potential.

See salaries and explore the career path for Prompt Engineer

AI Solutions Architect

An AI Solutions Architect designs and implements AI solutions for businesses. The DeepSeek course introduces them to DeepSeek AI and its potential applications. By differentiating between DeepSeek models and understanding their architectures, the AI Solutions Architect can make informed decisions about which models to use for different solutions. The course can help AI solutions architects by showing them how to make API calls to DeepSeek platform and how to perform DeepSeek integration in applications. An AI Solutions Architect who wants to use DeepSeek may find this course helpful.

See salaries and explore the career path for AI Solutions Architect

Data Scientist

A data scientist analyzes large datasets to extract meaningful insights and develop data driven solutions. This course about DeepSeek models helps them understand the latest advancements in AI. Data scientists can use the models to augment their analysis. Those wishing to train custom models or fine tune existing ones can use the the knowledge gained from this course to better understand model bias, the open source aspects, and distilled models. Therefore, data scientists can use this course to stay ahead of the curve regarding the capabilities of large language models.

See salaries and explore the career path for Data Scientist

Software Engineer

A Software Engineer designs, develops, and tests software applications. A software engineer considering integrating AI into their applications can learn how to do so with DeepSeek models. With this course, a Software Engineer can learn how to make API calls and integrate DeepSeek with LangChain. Additionally, the the information on setting up local Python development can help a Software Engineer integrate DeepSeek into their workflow. Software Engineers looking to incorporate DeepSeek-powered features into their projects may find this course useful.

See salaries and explore the career path for Software Engineer

Research Scientist

A Research Scientist conducts research to advance knowledge in a particular field. The DeepSeek course may help those interested in natural language processing and large language models. Research scientists can use the knowledge gained to better understand DeepSeek's architecture and compare it with models like OpenAI. The course also shows how to run DeepSeek locally allowing for experimentation and research. Research Scientists looking for an introduction to DeepSeek may find this course to be useful.

See salaries and explore the career path for Research Scientist

Data Analyst

A Data Analyst collects, processes, and analyzes data to identify trends and insights. This course on DeepSeek may be useful for data analysts who want to leverage AI to enhance their data analysis capabilities. By understanding the core concepts behind DeepSeek AI models, a data analyst can explore ways to automate data processing tasks, generate insights from complex datasets, and improve the accuracy of data-driven decision-making. Data Analysts who want to integrate AI into their workflows may find this course helpful.

See salaries and explore the career path for Data Analyst

AI Product Manager

An AI Product Manager is responsible for the strategy, roadmap, and execution of AI-powered products. This course on DeepSeek may be useful for an AI product manager to understand the capabilities and limitations of DeepSeek models. By differentiating between DeepSeek models, such as V3 and R1, and understanding their architectures, an AI product manager can make informed decisions about product features and development priorities. Getting familiar with the pricing and privacy policies can also inform product strategy. An AI Product Manager who wants to build products using DeepSeek can find this course helpful.

See salaries and explore the career path for AI Product Manager

Technical Writer

A Technical Writer creates documentation and guides for technical products and services. This course on DeepSeek models may be useful for a Technical Writer who needs to document DeepSeek's features, API, and usage. By understanding the models and how they work, a Technical Writer can create accurate and informative documentation for developers and users. Technical Writers can also learn how to set up billing for the DeepSeek platform and API status. Technical Writers who are tasked with documenting DeepSeek may find this course useful.

See salaries and explore the career path for Technical Writer

AI Trainer

An AI Trainer is responsible for training and fine-tuning AI models to improve their performance. This course on DeepSeek models may be useful for an AI trainer who wants to learn how to work with DeepSeek models. The course covers the core concepts behind DeepSeek models, including their architecture and censorship policies. The course also shows how to control the model behavior with inference parameters. AI Trainers wishing to use DeepSeek may find this course helpful.

See salaries and explore the career path for AI Trainer

Machine Learning Operations Engineer

A Machine Learning Operations Engineer (MLOps Engineer) focuses on deploying and maintaining machine learning models in production environments. This course about DeepSeek may be useful in understanding how to deploy DeepSeek models. The course explores how to make API calls to DeepSeek platform, privacy policy for the DeepSeek platform, and local API calls with the Ollama API. MLOps Engineers looking to use DeepSeek may find this course to be useful.

See salaries and explore the career path for Machine Learning Operations Engineer

Technical Consultant

A Technical Consultant provides expert advice and guidance to clients on technical issues. This course covering DeepSeek models may be useful in understanding DeepSeek and advising clients on its use. The course covers the models and how to classify them, how to enhance applications with APIs, how to perform DeepSeek integration in applications, and how to build user interfaces in Gradio. Technical Consultants who want to advise clients on DeepSeek may find this course to be useful.

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Quality Assurance Engineer

A Quality Assurance Engineer tests software and applications to ensure they meet quality standards. This course on DeepSeek may be useful for Quality Assurance Engineers who need to test AI-powered applications that use DeepSeek models. By understanding the core concepts behind DeepSeek models, QA Engineers can develop effective test cases to identify potential issues and ensure the quality of the application. QA Engineers working on applications with DeepSeek may find this course helpful.

See salaries and explore the career path for Quality Assurance Engineer

Chief Technology Officer

A Chief Technology Officer (CTO) is responsible for overseeing the technological direction of an organization. This course on DeepSeek could be helpful to CTOs who need to understand the capabilities and potential of new AI technologies. The course covers a range of topics from basic AI terminology to advanced concepts like running self-hosted model APIs, which could inform strategic decisions about AI adoption within a company. CTOs seeking an overview of DeepSeek may find this course useful.

See salaries and explore the career path for Chief Technology Officer

Reading list

We've selected two 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 DeepSeek AI for Developers.

Natural Language Processing with Transformers,...

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Provides a comprehensive guide to using transformer models, which are the foundation of DeepSeek's architecture. It covers topics such as model fine-tuning, tokenization, and inference. While not directly about DeepSeek, it provides invaluable background on the underlying technology. It is best used as additional reading to deepen understanding.

Natural Language Processing with Transformers,...

Paperback

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Natural Language Processing with Transformers,...

Kindle Edition

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Generative AI with Python and TensorFlow 2

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Provides a practical guide to building generative AI models using Python and TensorFlow 2. While it doesn't focus specifically on DeepSeek, it covers the fundamental concepts and techniques used in generative AI, which are relevant to understanding DeepSeek's models. It is best used as additional reading to broaden your knowledge of generative AI.

Generative AI with Python and TensorFlow 2: Create...

Paperback

DeepSeek AI for Developers

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Practical introduction to deepseek ai models

Activities

Career center

Reading list

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