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AI Engineering Bootcamp: Apps, RAG, Agents & MCP

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Move beyond basic chatbot tutorials and learn how to build complete, practical, and production-ready AI applications in just 14 days.AI Engineering Bootcamp: Apps, RAG, Agents & MCP is a hands-on, project-based course designed to take you from the foundations of Generative AI to advanced AI agents, Retrieval-Augmented Generation, Model Context Protocol, multi-agent systems, and production AI engineering.You will begin by understanding how Large Language Models, prompts, tokens, responses, and AI application architectures work. You will build your first terminal and Streamlit AI chatbot, create reusable LLM service layers, and learn how to connect Python applications to cloud-based or local AI models such as OpenAI and Ollama.The course then introduces practical prompt engineering techniques. You will learn how to structure prompts using roles, tasks, context, rules, examples, and output formats.

Through a hands-on prompt playground, you will experiment with reusable prompt templates and understand how better prompts lead to more reliable AI applications.Next, you will build real-world projects such as an AI Resume Analyzer, a PDF Chat Assistant, and an Autonomous Research Agent. You will learn how to process documents, extract text, create embeddings, split content into chunks, store vectors, and perform semantic search using tools such as ChromaDB.You will explore both beginner and advanced RAG systems. Topics include vector databases, hybrid retrieval, reranking, metadata filtering, query transformation, Knowledge Graph RAG, and Agentic RAG.

You will use these concepts to build an enterprise-ready AI Knowledge Assistant capable of answering questions from business documents and private data.The course also provides a deep introduction to AI agent engineering. You will learn how agents combine LLMs, tools, memory, planning, workflows, and state to complete complex tasks. You will build autonomous agents that can research topics, generate reports, use external tools, interact with websites, and evaluate their own outputs.You will also build multi-agent AI systems using orchestration patterns such as planner, researcher, analyst, writer, and reviewer.

You will explore LangGraph, agent communication, shared state, task delegation, and enterprise multi-agent architectures.A major part of the course focuses on the Model Context Protocol, commonly known as MCP. You will learn how MCP allows AI applications to connect securely with tools, files, APIs, databases, and enterprise systems. You will build your own MCP server and understand how to design multi-server MCP ecosystems.Additional topics include browser agents, multimodal AI, vision models, voice AI, evaluation, monitoring, observability, guardrails, responsible AI, governance, deployment, and production reliability.By the end of this course, you will have built a strong portfolio of AI engineering projects, including chatbots, RAG applications, autonomous agents, browser automation systems, MCP tools, multi-agent workflows, and a complete production AI platform.This course is ideal for Python developers, AI enthusiasts, software engineers, data professionals, students, and anyone who wants to become an AI Engineer, Generative AI Developer, RAG Developer, or Agentic AI Engineer through practical, hands-on learning.

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Unlock the full potential of data science and artificial intelligence with “Mastering Data Science & AI with Python”—a comprehensive, beginner-to-advanced course designed to transform you into a job-ready data scientist and AI developer. Whether you're just starting your coding journey or looking to upskill and build real-world AI-powered applications, this course covers everything you need in one complete package.We begin with the fundamentals of Python, ensuring you're well-equipped with programming basics and critical libraries like NumPy and Pandas. You'll quickly progress to manipulating and analyzing data efficiently, including accessing, cleaning, and filtering DataFrames. Learn how to visualize your insights with powerful charts and graphs that bring your data to life.From there, you’ll dive deep into statistics for data science, the cornerstone for understanding machine learning. You'll master core statistical concepts essential for model development and evaluation. The machine learning section then takes you through supervised and unsupervised learning—covering regression, binary and multiclass classification, clustering algorithms, and dimensionality reduction techniques like t-SNE and PCA.Hands-on practice is the heart of this course. You’ll complete 9 end-to-end projects that simulate real industry scenarios:Automate business workflows with PandasAnalyze large datasets with Google AppsBuild a movie recommendation engine using Non-negative Matrix FactorizationDevelop predictive models and evaluate them using advanced techniquesBuild and deploy a credit risk prediction app with XGBoost and StreamlitCreate LLM-powered AI apps using Ollama, LangChain, and Streamlit—no cloud requiredImplement local Python libraries for AI interactionsWhat truly sets this course apart is its focus on local LLMs and AI automation tools. You’ll explore cutting-edge frameworks like Ollama, interact with models through Web UI, LM Studio, and even build your own AI Code Assistant and RAG-based AI Research App—equipping you with the skills to develop, test, and deploy modern AI systems without relying on expensive APIs or cloud services.All modules are crafted with a blend of theory, code-alongs, and practical exercises. You'll walk away not only with technical knowledge but also a portfolio of working applications that showcase your expertise in Python programming, machine learning, and AI.By the end of this course, you will be able to:Code in Python with confidenceAnalyze, visualize, and model data effectivelyUnderstand and implement ML algorithms from scratchBuild real-world projects that demonstrate your skillsDevelop and deploy AI apps using local LLMs and tools like Ollama and StreamlitThis course is ideal for aspiring data scientists, developers, and AI enthusiasts eager to build practical, high-impact solutions. If you're looking to transition into tech, upgrade your skills, or break into AI development, this is the only course you’ll need.

Machine Learning Mastery: From Basics to Advanced Techniques

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Are you ready to dive into the exciting world of machine learning? Look no further! In this comprehensive Udemy course, you’ll learn everything you need to know about machine learning, from foundational concepts to cutting-edge techniques.Are you ready to embark on an exhilarating journey into the world of machine learning? Look no further! Our comprehensive Udemy course, “Machine Learning Mastery: From Basics to Advanced Techniques,” is designed to empower learners of all levels with the knowledge and skills needed to thrive in this dynamic field.In this course, we demystify machine learning concepts, starting from the fundamentals and gradually progressing to advanced techniques. What You’ll Learn:Understand the fundamentals of supervised and unsupervised learningExplore popular machine learning algorithms.Use natural language processing (NLP) with Supervised Machine Learning Algorithms for Sentiment Analysis & Text Classification.Implement real-world projects using Python and scikit-learn.Optimize models for accuracy and efficiency.Why Take This Course?Practical experience: Learn by doing with hands-on projects and exercises.Portfolio building: Showcase your skills to potential employers.Problem-solving: Develop critical thinking skills to tackle real-world challenges.Continuous learning: Stay updated with the latest advancements in machine learningWhether you’re a beginner or an experienced data scientist, this course will empower you to create intelligent solutions and make an impact in the field of machine learning. Enroll now and start your journey toward becoming a machine learning pro! Here’s what you can expect:Foundational Knowledge:Understand the core principles of supervised and unsupervised learning.Explore regression, classification, clustering, and dimensionality reduction.Algorithm Deep Dive:Dive into popular machine learning algorithms, including linear regression, decision trees, support vector machines, and neural networks.Learn how to choose the right algorithm for specific tasks.Real-World Applications:Apply your knowledge to real-world projects using Python and libraries like scikit-learn.Tackle natural language processing (NLP) challenges with Supervised ML Algorithms for Sentiment Analysis & Text Classification.Model Optimization:Discover techniques for model evaluation, hyperparameter tuning, and performance optimization.Learn how to avoid common pitfalls and enhance model accuracy.Career Boost:Build a strong portfolio by completing hands-on exercises and projects.Gain practical experience that sets you apart in job interviews.Stay Current:Keep pace with the ever-evolving field of machine learning.Stay informed about the latest research and trends.Whether you’re a data enthusiast, aspiring data scientist, or seasoned professional, this course provides a solid foundation and equips you with practical skills. Enroll now and unlock the potential of machine learning!

Practice Exams: AI Engineer Most Asked Interview Questions.

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Are you preparing for an AI Engineer role at a top tech company and want to know if you'll pass the technical interview? This practice exam course is your unfair advantage. Built around the most frequently asked questions in real AI Engineering interviews, this course contains 6 full-length, timed practice tests covering every critical domain a modern AI Engineer is expected to master — from foundational Python tooling to advanced MLOps, cloud deployment, and LLM serving infrastructure. * WHAT'S INSIDE — 6 PRACTICE EXAMSPractice Exam 1 — Python Stack for AI Engineers NumPy, Pandas, PyTorch, TensorFlow, FastAPI, MLflow, scikit-learn, data pipelines, vectorized operations, transfer learning, and production deployment patterns. Practice Exam 2 — Machine Learning Stack Supervised & unsupervised learning, model evaluation, feature engineering, drift detection, experiment tracking, MLOps lifecycle, and deployment strategies. Practice Exam 3 — Docker & Kubernetes for AI Containerization best practices, image optimization, Kubernetes resource management, GPU scheduling, Helm, service meshes, liveness/readiness probes, and CI/CD pipelines. Practice Exam 4 — Vector Databases Embeddings, approximate nearest neighbor (ANN) search, HNSW, IVF, cosine similarity, RAG architecture, semantic search, quantization, and production vector store operations. Practice Exam 5 — APIs & Microservices for AI Engineers REST design, gRPC, GraphQL, API Gateway, rate limiting, authentication (OAuth2, JWT, mTLS), circuit breakers, Saga pattern, event-driven architecture, CQRS, and LLM API integration. Practice Exam 6 — Cloud Platforms (AWS, Azure, GCP) SageMaker, Azure ML, Vertex AI, managed inference endpoints, feature stores, MLOps pipelines, IoT Edge deployment, drift monitoring, and cloud-native AI architecture. * WHY THIS COURSE WORKS Every question includes a detailed explanation for both the correct answer and every distractor, so you understand not just what is right, but why every wrong option fails. This is the fastest way to close knowledge gaps before a real interview. Questions are designed across three difficulty styles: - Direct conceptual questions — test your definitions and mental models - Indirect/scenario-based questions — test your applied engineering judgment - Multi-select questions — test your ability to identify all valid answers simultaneously.* WHO IS THIS COURSE FOR? AI/ML engineers preparing for technical interviews at FAANG, startups, or AI-first companies → Software engineers transitioning into AI Engineering roles → MLOps engineers validating their end-to-end production knowledge → Data scientists moving toward deployment and infrastructure responsibilities → Anyone self-studying AI Engineering who wants to benchmark their readiness * WHAT YOU'LL VALIDATEAfter completing all six practice exams, you will have stress-tested your knowledge across every layer of the modern AI Engineer stack: data handling, model training, experiment tracking, serving infrastructure, API design, containerization, orchestration, cloud deployment, vector search, and LLM integration. You'll know exactly which topics need more study before your real interview — and you'll walk in confident about the rest. No video lectures. No filler. Just high-quality interview questions, expert explanations, and measurable exam-readiness.

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AI Engineering Bootcamp: Apps, RAG, Agents & MCP

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AI Engineering Bootcamp: Apps, RAG, Agents & MCP

About this course

Skills you'll gain

Available Coupons

Course Information

This course includes:

Share this course

You May Also Like

Mastering Data Science & AI with Python & Real-World Project

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