What you will learn
- Understand the impact of DeepSeek in the generative AI landscape, including its advantages, limitations, and usage best practices
- Explore the DeepSeek interface with hands-on tests, file attachment reading, image processing, and web search
- Master prompt engineering techniques like role prompting and in-context learning, applied to real-world tasks
- Use DeepSeek for content and code generation, translation, proofreading, tone adjustment, and logical problem-solving
- Run DeepSeek locally with full privacy using tools like LM Studio, Ollama, and Google Colab
- Use DeepSeek via API and in environments like VS Code, regardless of your hardware
- Learn how to download models directly from repositories like Hugging Face and set them up properly
- Integrate DeepSeek with spreadsheets, emails, and forms to build useful automations without writing any code
- Develop real projects such as automatically categorizing form data and logging results into spreadsheets
- Build an application using RAG with DeepSeek and Streamlit to create smart document interactions
Requirements
- Basic Python programming
Description
Learn everything from the basics to advanced techniques to master DeepSeek, one of today’s most powerful open-source Generative AIs — capable of outperforming even the most advanced ChatGPT models. In this course, you’ll explore how to use this revolutionary technology in practice, both with and without programming, always focusing on real examples and useful applications for personal and professional use.
You’ll gain a clear understanding of DeepSeek’s impact in the AI landscape, along with its advantages, limitations, and best practices. We start with the interface, including hands-on tests, file reading, image processing, and web search. You’ll dive into prompt engineering with techniques like role prompting and in-context learning, and see how they can be effectively applied to tasks such as content and code generation, translation, proofreading, tone adjustment, and logical problem-solving.
Learn through real examples: content creation, math exercises, code generation, and application development. Discover how to download and run the model locally on your own computer — fully private and without requiring an internet connection to run the model. You’ll learn how to use DeepSeek through Graphical User Interfaces (using LM Studio), via API, on Google Colab, with Ollama, and with VS Code — regardless of your hardware. You’ll also learn how to choose and download models directly from repositories like Hugging Face.
Additionally, you’ll learn how to integrate DeepSeek with services such as forms, spreadsheets, and email, enabling practical automations without needing to write any code. One of the projects developed will be a fully functional solution that reads contact form data, automatically categorizes the information, and records the results in a Google Sheet.
By the end of the course, you’ll know how to develop a real-world project using RAG (Retrieval-Augmented Generation), where DeepSeek interacts with documents through a modern Streamlit interface, ideal for those looking to build full-featured AI-powered applications.
Whether you’re a coder or not, this course is your gateway to unlocking the full potential of DeepSeek and turning ideas into powerful AI-driven solutions.
Who this course is for
- Anyone interested in Artificial Intelligence who wants to learn how to use generative AIs — with or without coding
- Developers looking to explore DeepSeek’s potential and run it locally with full control
- Professionals who want to use DeepSeek to generate ideas, improve writing, and translate more efficiently
- Developers aiming to integrate DeepSeek into their own applications, without relying on paid APIs
- Students and self-learners who want to understand and experiment with generative AI through real-world use cases
- Tech professionals or enthusiasts looking to connect DeepSeek with tools like spreadsheets, forms, and web apps
- Individuals who want to master open-source models like DeepSeek without being limited to closed solutions like ChatGPT
