Descriptions
The Complete Full Stack AI Engineering Bootcamp, Welcome to the step-by-step roadmap to becoming an AI engineer. This course takes you from fundamentals to advanced, real-world AI engineering with a clear learning path: you will build systems, understand why they work, and learn how AI, data, machine learning, deep learning, and LLM engineering connect together into production-grade workflows used in industry.
Topics and outcomes include Python programming (beginner to advanced) for AI; data representation with vectors and matrices; data visualization, statistics, and feature engineering; SQL and PostgreSQL; supervised and unsupervised machine learning (regression, SVM, decision trees, XGBoost, K-means, DBSCAN); building APIs with FastAPI and running AI apps in Docker; deploying models to production; data engineering fundamentals with Kafka and Spark; deep learning foundations (ANN, CNN, RNN/LSTM/GRU) and PyTorch from scratch; NLP fundamentals (tokenization, embeddings) and transformer architecture; LLM engineering including RAG, LangChain, LangGraph, AI agents and LLM workflows; and Model Context Protocol (MCP) with local MCP server and client examples. The course is theory plus hands-on: you will write code, build projects, deploy models, and gain practical, industry-relevant skills to transition your career into AI engineering.
What you’ll learn
- Build end to end AI Engineering projects using Python, PyTorch, scikit-learn, and SQL from data processing to model deployment.
- Master Natural Language Processing (NLP) and Transformers by implementing real projects with Hugging Face, BERT, T5, and Large Language Models (LLMs).
- Develop production ready AI APIs using FastAPI and Docker for scalable model deployment.
- Understand and implement LangChain and LangGraph to build multi-agent LLM applications with memory, tools, and workflows.
- Learn Model Context Protocol (MCP) and create MCP servers and clients for advanced AI tool integration.
- Perform data analysis, visualization, and feature engineering using Matplotlib and scikit-learn for machine learning pipelines.
- Design AI systems with context engineering, prompt engineering, RAG, and memory management.
- Gain practical skills required for AI Engineer, NLP Engineer, and LLM Engineer roles in the industry.
Who this course is for
- Beginners who want a clear and structured roadmap to enter the field of AI and machine learning from scratch
- Software developers and full stack developers who want to transition into AI engineering and learn how real AI systems are built and deployed
- Data analysts, data engineers, and aspiring data scientists who want to strengthen their practical AI and deep learning skills
- Students and fresh graduates who want to become job ready AI engineers
Specificatoin of The Complete Full Stack AI Engineering Bootcamp
- Publisher : Udemy
- Teacher : Aritra Basak
- Language: English
- Level : All Levels
- Lectures : 338
- Duration : 55 hours and 44 minutes
Content of The Complete Full Stack AI Engineering Bootcamp
Requirements
- No prior knowledge of AI or machine learning is required. Everything is taught from the ground up
- A laptop or desktop with internet connection to run Python, VS Code, and required tools
Pictures
Sample Clip
Installation Guide
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Subtitle : English
Quality: 1080p
The 2026/5 version has increased the number of lessons by 46 and the duration increased by 8 hours 39 minutes compared to 2026/2.
Download Links
Password file(s): www.downloadly.ir
File size
17.27 GB
