The AI Development with DeepSeek for Developers course is a hands-on guide to building real-world AI applications using DeepSeek's open-source models—no prior experience required. Designed for developers, students, ML engineers, and tech enthusiasts, this course combines conceptual overviews with practical projects, delivered through Jupyter notebooks and popular Python libraries like LangChain, Streamlit, Promptify, DSPy, and DeepEval.
AI Development with DeepSeek for Developers
AI Development with DeepSeek for Developers
Instructor: Board Infinity
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Build Q&A, chatbot, and code generation apps using DeepSeek models and practical Python tools.
Apply DeepSeek for document summarization, long-context analysis, and mathematical reasoning in real-world projects.
Create task-specific AI agents, compare DeepSeek’s performance and pricing with OpenAI, Claude, and Gemini, and interpret benchmark results.
Customize, fine-tune, and evaluate DeepSeek models using advanced features like GRPO, MoE, quantized models, and DeepEval metrics.
Skills you'll gain
Details to know
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Assessments
16 assignments
Taught in English
Recently updated!
July 2025
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There are 4 modules in this course
In the first module, you’ll get introduced to DeepSeek’s open-source model ecosystem and learn how to access and use the models via the web interface and API. You’ll explore the differences between DeepSeek-V3, R1, and Coder, and build your first simple app using Promptify and Streamlit. This module builds your foundation for hands-on work with the models.
What's included
13 videos4 readings4 assignments1 discussion prompt2 plugins
This module focuses on applying DeepSeek to solve real-world tasks. You’ll use DeepSeek models for document retrieval, summarization, code generation, and building interactive Q&A systems. This module blends conceptual understanding with implementation using LangChain, DSPy, and Jupyter notebooks.
What's included
9 videos3 readings4 assignments1 plugin
In the third module, you’ll build practical task-based agents using DeepSeek and LangChain. You’ll also evaluate how DeepSeek compares with other leading models like GPT-4o, Claude, and Gemini in terms of pricing, benchmark performance, and capabilities. This module gives you insight into model selection and real-world deployment tradeoffs.
What's included
9 videos3 readings4 assignments1 plugin
In the final module, you’ll explore DeepSeek’s unique innovations — from Guided Roleplay Optimization (GRPO) and Mixture-of-Experts to fine-tuning and quantized models for efficient inference. You’ll also use tools like DeepEval to assess model output quality and benchmark performance. This module helps you understand DeepSeek’s strengths and customization potential.
What's included
11 videos3 readings4 assignments1 plugin
Instructor
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