DeepSeek AI: From Fundamentals to Applications

Edureka via Coursera Specialization

Go to class Write review

Details

Go to class

Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 weeks, 5 hours a week
Sessions

Self-Paced
Level

Beginner

Found in

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

Model choice is now an architecture decision, not a default. DeepSeek gives teams open weight models they can call through a hosted API or run on their own infrastructure, and this Specialization covers building applications on either. You start with API integration, prompt design, and a conversational developer assistant. You then make responses reliable through schemas, validation, tool calling, and retrieval, and finish with agents that plan, act under human approval, and run behind a deployed endpoint. By the end of this Specialization, you will be able to: • Integrate the DeepSeek API with secure key handling and resilient request logic. • Design and version prompts for explanation, debugging, refactoring, and testing. • Return validated structured output using JSON schemas and Pydantic models. • Connect calculators, external APIs, and databases through function calling. • Build retrieval augmented generation grounded in your own document collections. • Deploy an evaluated, containerized agent behind a FastAPI service. This Specialization suits software developers, AI engineers, backend engineers, and technical leads assessing open weight models for real workloads. It assumes working Python and comfort calling APIs, and no background in LLM application development, retrieval, or agents. Enroll now to build DeepSeek applications you can evaluate, secure, and deploy.

Syllabus

Course 1: Introduction to DeepSeek
Course 2: RAG and Tool Calling with DeepSeek
Course 3: Developing AI Agents with DeepSeek

Courses

0 reviews

View details

DeepSeek applications are built differently from traditional software because model behavior, prompts, context, API reliability, and response quality all become part of application design. In this hands-on course, you’ll learn Introduction to DeepSeek, a practical course that helps developers integrate DeepSeek into Python applications, design effective prompts, build developer utilities, and create reliable conversational AI experiences. Whether you want to understand DeepSeek models, make secure API calls, automate coding tasks, or build context-aware assistants, this course gives you a structured starting point. You’ll begin by exploring the foundations of DeepSeek, including model capabilities, developer use cases, API architecture, request lifecycles, Python environments, dependency setup, API key security, and your first DeepSeek API request. Then, you’ll move into prompt engineering and developer utilities by working with prompt roles, context, constraints, code explanation, debugging, refactoring, unit-test generation, reusable prompt templates, and prompt testing practices. Finally, you’ll explore conversational application development using message history, context windows, trimming, summarization, streaming responses, timeout handling, retry strategies, and end-to-end testing of a DeepSeek-powered developer assistant. By the end of this course, you will be able to: -Explain DeepSeek models, developer use cases, API architecture, request lifecycles, and core application concepts. -Configure a Python development environment, manage dependencies, secure API credentials, and execute DeepSeek API requests. -Design effective prompts using roles, context, constraints, examples, and reusable prompt templates for developer-focused tasks. -Build DeepSeek-powered utilities for code explanation, debugging, refactoring, and unit-test generation. -Develop reliable conversational applications using message history, context management, streaming, timeouts, retries, and response testing. This course is designed for Python developers, backend engineers, AI application developers, software engineers, technical professionals, and anyone who wants to understand how DeepSeek can be integrated into practical development workflows. If you are new to DeepSeek or want a guided path from basic API integration to prompt engineering and conversational application development, this course provides a practical learning experience. You should have basic familiarity with Python and general programming concepts. Experience with virtual environments, APIs, environment variables, Git, and command-line usage is helpful, along with a willingness to practice through hands-on development tasks. Enroll now and learn how to build secure, reliable, and practical AI-powered applications with DeepSeek.
0 reviews

View details

AI applications are evolving beyond traditional chatbots into systems that can reason, use tools, manage state, and perform multi-step tasks. In this hands-on course, you’ll learn Developing AI Agents with DeepSeek, a practical course that helps developers build, control, evaluate, secure, and deploy AI agents using DeepSeek and modern agentic application patterns. Whether you want to understand agentic workflows, connect external tools, implement MCP, improve agent reliability, or deploy AI applications with FastAPI and Docker, this course gives you a structured starting point. You’ll begin by exploring the foundations of AI agents, including agentic workflows, agent control, state, execution patterns, ReAct, Plan-and-Execute, Router agents, execution loops, tool calling, and the Model Context Protocol (MCP). Then, you’ll move into agent reliability and control by working with state management, task progress, iteration limits, failure recovery, human-in-the-loop approvals, multiple MCP tools, local DeepSeek models, and evaluation datasets. Finally, you’ll explore automated response evaluation, accuracy, relevance, groundedness, tool success metrics, prompt injection, data leakage, tool misuse, model size and quantization, FastAPI endpoints, request validation, health checks, and Docker-based deployment. By the end of this course, you will be able to: -Explain AI agents, agentic workflows, execution patterns, agent state, and the differences between chatbots, workflows, and agents. -Buildcontrolled DeepSeek agents using execution loops, tool calling, MCP integration, state management, and task progress tracking. -Implement agent reliability and control mechanisms using iteration limits, failure recovery, execution boundaries, and human-in-the-loop approvals. -Evaluate DeepSeek applications using structured datasets, automated response checks, accuracy, relevance, groundedness, and tool success metrics while identifying prompt injection, data leakage, and tool misuse risks. -Deploy DeepSeek applications through FastAPI and Docker using request validation, error handling, health checks, and production-oriented configuration. This course is designed for AI developers, Python developers, backend engineers, generative AI developers, software engineers, and anyone who wants to understand how reliable and controlled AI agents are designed, evaluated, secured, and deployed. If you are new to agentic AI or want a practical path from basic DeepSeek integration to tool-enabled and deployment-ready AI agents, this course provides a guided learning experience. You should have basic experience with Python and generative AI concepts. Familiarity with APIs, JSON, command-line usage, and Docker is helpful, along with a willingness to practice through hands-on agent development, evaluation, security testing, and deployment tasks. Enroll now and learn how to build controlled, reliable, secure, and deployment-ready AI agents with DeepSeek.
0 reviews

View details

RAG and tool-enabled AI applications are built to do more than generate free-form responses. In this hands-on course, you’ll learn RAG and Tool Calling with DeepSeek, a practical course that helps developers create structured, reliable, tool-enabled, and knowledge-grounded AI applications. Whether you want to validate AI responses, connect models with external tools, retrieve information from documents, or reduce hallucinations, this course gives you a structured starting point. You’ll begin by exploring structured output and reliable response design, including JSON structures, schemas, reasoning modes, Pydantic models, field constraints, validation errors, response repair, and fallback strategies. Then, you’ll move into function calling and external tool integration by working with JSON Schema, calculator tools, external APIs, authentication, timeouts, restricted SQLite access, multi-tool routing, and failure recovery. Finally, you’ll explore Retrieval-Augmented Generation with document loading, text chunking, embeddings, vector stores, semantic retrieval, top-K selection, metadata filtering, grounded generation, source attribution, hallucination control, and RAG application testing. By the end of this course, you will be able to: -Design structured DeepSeek responses using JSON schemas, Pydantic models, field types, constraints, and validation rules. -Implement response validation, repair, and fallback strategies for reliable AI application workflows. -Integrate DeepSeek with external tools, APIs, and restricted databases using function calling, JSON -Schema, and safe execution practices. -Develop RAG pipelines using document processing, embeddings, vector stores, semantic retrieval, top-K selection, and metadata filtering. -Evaluate grounded DeepSeek responses for source relevance, hallucination control, unsupported questions, and overall application reliability. This course is designed for Python developers, AI application developers, backend engineers, software engineers, and anyone who wants to build reliable applications using DeepSeek. If you are familiar with basic DeepSeek API usage and want a practical path from structured responses to function calling and Retrieval-Augmented Generation, this course provides a guided learning experience. You should have basic experience with Python, JSON, APIs, and command-line usage. Familiarity with DeepSeek or other LLM APIs, Python virtual environments, and basic database concepts is helpful, along with a willingness to practice through hands-on AI application development tasks. Enroll now and learn how to build reliable, tool-enabled, and knowledge-grounded AI applications with DeepSeek.