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Beginner Artificial Intelligence 48 hours

Complete AI Engineer Bootcamp 2026: Python, NLP, LLMs, LangChain & Hugging Face

Master the Full AI Engineering Stack — From Python Fundamentals to Production-Ready LLM Applications with Transformers, RAG, and Real-World APIs

10 Chapters
40 Lessons
1223 min total
Open Access

This is the most comprehensive AI Engineering course available in 2026. You will go from absolute beginner to production-ready AI engineer, mastering every layer of the modern AI stack. Unlike courses that focus on a si...

What you'll learn

  • The AI Engineering Landscape in 2026
  • Python Foundations for AI Engineers
  • Data Processing and Feature Engineering for NLP
  • Natural Language Processing from Scratch
  • Deep Learning and Neural Networks for NLP
  • The Transformer Architecture Deep Dive

+ 4 more chapters below

Engr Mejba Ahmed

Engr. Mejba Ahmed

Course Instructor

Complete AI Engineer Bootcamp 2026: Python, NLP, LLMs, LangChain & Hugging Face

About This Course

This is the most comprehensive AI Engineering course available in 2026. You will go from absolute beginner to production-ready AI engineer, mastering every layer of the modern AI stack.

Unlike courses that focus on a single tool or framework, this bootcamp gives you the complete foundation — Python programming, data processing, classical NLP, deep learning, the Transformer architecture, Hugging Face, Large Language Models, LangChain, vector databases, and RAG — all connected through hands-on projects you can use in your portfolio.

What You Will Build

  • A sentiment analysis engine using classical NLP and scikit-learn
  • A text classifier powered by fine-tuned BERT
  • A custom chatbot using LangChain with memory and tool calling
  • A RAG application with Pinecone vector database and real-time document retrieval
  • A production AI API deployed with FastAPI, complete with authentication and monitoring

Who This Course Is For

  • Software developers wanting to transition into AI engineering
  • Data analysts looking to build production AI systems
  • Students and self-learners who want a structured, career-focused AI curriculum
  • Anyone who wants to understand AI deeply — not just use ChatGPT, but build systems like it

Prerequisites

  • Basic programming knowledge (any language) — we teach Python from scratch
  • A computer with internet access
  • Curiosity and willingness to practice

Why This Course?

AI engineers are the most in-demand professionals of 2026, with median salaries exceeding $185,000. This course does not just teach theory — every concept is immediately applied in code. By the end, you will have 5 portfolio-ready projects and the skills to build, fine-tune, and deploy AI systems professionally.

Course Curriculum

10 chapters 40 lessons 1223 min

2 lessons available to preview

1 Why AI Engineering Is the Highest-Demand Career of 2026
Preview 18min
2 The Complete AI Engineering Toolchain Explained
Preview 22min
3 Career Paths, Salaries, and Industry Demand Analysis
20min
4 Setting Up Your AI Development Environment
25min
1 Python Core Syntax and Data Types for AI Work
28min
2 Functions, Classes, and Error Handling Patterns
30min
3 NumPy and Pandas — Data Manipulation at Scale
35min
4 Working with APIs, JSON, and File I/O for AI Pipelines
30min
1 Text Preprocessing — Cleaning Raw Text for NLP Models
30min
2 Tokenization Deep Dive — From Words to Subwords
28min
3 Text Vectorization — TF-IDF, Bag of Words, and Beyond
25min
4 Word Embeddings — Word2Vec, GloVe, and Semantic Representations
32min
1 Sentiment Analysis — Building Your First NLP Application
35min
2 Text Classification with scikit-learn — Multi-Class and Multi-Label
30min
3 Named Entity Recognition with spaCy
28min
4 Topic Modeling and Text Clustering — Discovering Hidden Patterns
30min
1 Neural Network Foundations — From Perceptrons to Deep Learning
35min
2 PyTorch Fundamentals — Tensors, Autograd, and Training Loops
32min
3 Recurrent Neural Networks and LSTMs for Sequential Data
30min
4 The Attention Mechanism — The Breakthrough That Changed Everything
35min
1 The Transformer Architecture — Complete Technical Breakdown
35min
2 BERT Deep Dive — Pre-Training, Fine-Tuning, and Applications
30min
3 GPT Architecture — How Autoregressive Language Models Generate Text
32min
4 Encoder vs Decoder vs Encoder-Decoder — Choosing the Right Architecture
28min
1 Hugging Face Transformers Library — Pipelines and Model Hub
30min
2 Fine-Tuning Pre-Trained Models with the Trainer API
35min
3 Sentence Transformers — Semantic Search and Similarity
30min
4 Token Classification, Question Answering, and Advanced HF Tasks
28min
1 Working with LLM APIs — OpenAI, Anthropic, and Open-Source Models
32min
2 Prompt Engineering for Developers — Structured Outputs and Few-Shot Learning
30min
3 Function Calling and Tool Use with LLMs
32min
4 Evaluating and Comparing LLM Performance — Benchmarks and Testing
28min
1 LangChain Fundamentals — Models, Prompts, Chains, and LCEL
35min
2 Conversational Memory and Chat History Management
30min
3 LangChain Agents — Autonomous AI with Tool Integration
35min
4 LangGraph — Building Complex AI Workflows with State Machines
35min
1 Vector Databases Explained — Embeddings, Indexing, and Semantic Search
35min
2 Building Production RAG Systems — Retrieval-Augmented Generation
40min
3 Deploying AI Applications with FastAPI
35min
4 Monitoring, Observability, and Your AI Engineering Career Next Steps
30min
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