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The Complete AI & GenAI Engineer Bootcamp 2026: Zero to Hero

Name: The Complete AI & GenAI Engineer Bootcamp 2026: Zero to Hero
Rating: 4.7 (24 reviews)

Machine Learning, Deep Learning, NLP, LLMs, RAG, AI Agents, Hugging Face, LangChain, LangGraph, Build Real AI Projects

Created byAI University

Last updated 5/2026

English

What you'll learn

Build Machine Learning models from scratch using Python, NumPy, Pandas, and Scikit-Learn for real-world tasks like prediction, classification, and clustering.
Understand Deep Learning and Neural Networks with TensorFlow and Keras, including CNNs for image data and RNN/LSTM for sequence data.
Learn Natural Language Processing (NLP) including text preprocessing, embeddings, and transformer models like BERT.
Work with modern Generative AI tools such as Hugging Face, Transformers, and advanced prompt engineering techniques.
Build real-world GenAI applications including RAG systems, LLM chatbots, and AI agents using LangChain and LangGraph.
Create portfolio-ready AI projects with interactive interfaces using Streamlit and Gradio.

Course content

20 sections • 90 lectures • 48h 41m total length

Course Introduction3:42
Explore practical AI and Gen AI engineering by building real AI applications, from Python foundations to ML, DL, NLP, transformers, and AI agents using tools like Hugging Face.
What is an AI Engineer8:21
Tools and Technologies5:02

Python Basics Refresher47:40
Working With Arrays Using NumPy1:28:02
Learn to manipulate and analyze data with numpy arrays, mastering creation, indexing, and operations essential for building reliable ai and genai workflows.
Data Handling With Pandas1:03:07
Master data handling with pandas to perform efficient data manipulation and analysis in the AI and GenAI engineer bootcamp.
Visualizing Datasets With Matplotlib & Seaborn23:05
EDA Project26:09
Resource

Supervised vs Unsupervised Learning41:26
Compare supervised and unsupervised learning to understand how each approach drives AI and GenAI projects.
Regression Models1:10:49
Master regression models as a core tool in the AI and GenAI engineer bootcamp. Build practical skills in modeling relationships and making predictions.
Classification Models1:11:27
Explore classification models within the complete AI and GenAI engineer bootcamp, introducing core concepts, methods, and practical applications for building reliable classifiers.
Underfitting vs Overfitting19:29
Visually and numerically compare underfitting, overfitting, and good fit to understand how model complexity affects performance. Learn to balance bias and variance to generalize to unseen data.
Model Evaluation Techniques22:48
House Price Prediction13:39
Build a house price prediction system with linear regression, using income, age, rooms, bedrooms, and population as features; clean data, train, and evaluate with MAE, RMSE, and R2.
Spam Email Classification12:47
Build a spam email classification system using machine learning and natural language processing to distinguish spam from ham with a binary classifier based on bag-of-words and multinomial Naive Bayes.
Customer Segmentation (Clustering)18:57
Learn customer segmentation with unsupervised machine learning using income and spending score, scaled for fair clustering with k-means and the ELBO method to find the optimal k.
Resource

Neural Networks Intuition29:38
Backpropagation38:01
Explore backpropagation fundamentals to train neural networks within the complete AI and GenAI engineer bootcamp, from zero to hero.
Activation & Loss Functions50:07
Master activation functions and loss functions in AI engineering, and learn how these components shape model performance.
CNNs For Image Data35:19
Explore convolutional neural networks for image data, covering core concepts, architectures, and practical applications within an ai engineering bootcamp.
RNN / LSTM For Sequences38:42
Explore recurrent neural networks and long short-term memory models for sequence data, highlighting how these architectures capture dependencies over time.
Intro To Transformers39:03
Dive into transformers for AI and GenAI, introducing the basics of transformer concepts and their applications in building intelligent models.
CNN Based Image Classifier Using TensorFlow22:23
Build a CNN-based image classifier using TensorFlow to detect whether a person wears a mask, train with a face mask dataset, and save the trained model for real-time use.
Resource

Text Preprocessing11:24
TF-IDF vs Embeddings47:40
Explore TF-IDF and embeddings to compare traditional text representations and modern neural methods, within the complete AI and GenAI engineer bootcamp from zero to hero.
Word Embeddings15:10
BERT & Encoder Models51:47
Explore BERT and encoder models to understand how they process language tasks. Learn their role in advancing AI and GenAI applications in this bootcamp.
Common NLP Tasks37:15
Explore common NLP tasks and build the foundation for a career as an AI and GenAI engineer.
Sentiment Analysis System32:47
Resource

Image Processing Basics14:03
Understand image processing as a foundational artificial intelligence and computer vision technique that uses algorithms to analyze and enhance images, detect edges, and extract objects, faces, and text.
CNN and ResNet18:03
Learn how convolutional neural networks process images by detecting edges and building complex features. Understand residual connections in ResNet to enable training of very deep networks.
Object Detection (YOLO)28:51
Image Classification Model49:15
Explore building an image classification model within a comprehensive ai and genai engineering bootcamp, from foundations to practical implementation.
Resource

What LLMs are48:25
Explain what LLMs are and how they fit into AI and GenAI engineering within a comprehensive bootcamp.
GPT vs Claude vs LLaMA37:03
Compare GPT, Claude, and LLaMA to explore their capabilities and roles in the AI and GenAI engineer bootcamp.
Tokens and Context Windows40:07
Explore how tokens and context windows influence prompt design and model reasoning, enabling efficient input handling and more accurate AI and GenAI outputs.
Open vs Closed Models34:34
Explore open vs closed models in this complete AI and GenAI engineer bootcamp module, contrasting approaches and implications for building AI solutions.

Self Attention1:12:32
Explore self-attention as a core mechanism in transformer models, essential for AI and GenAI engineering.
Encoder vs Decoder46:54
Explore the roles of encoders and decoders in AI and GenAI, and learn how these components drive information encoding and reconstruction.
Why Transformers Scale43:28
Explore why transformers scale and how this topic shapes learning in the complete ai and genai engineer bootcamp.

Transformers Library21:23
Explore the transformer library, a Python toolkit on PyTorch and TensorFlow. Download, train, and fine-tune large pre-trained models for NLP tasks via tokenizer, model, and pipeline.
Tokenizers44:58
Explore tokenizers in the context of the complete ai and genai engineer bootcamp from zero to hero.
Loading Models41:59
Explore loading models within the complete ai & genai engineer bootcamp from zero to hero.
Creating Embeddings30:58
Resource

Requirements

Basic knowledge of Python is helpful but not mandatory.
No prior experience is required. We'll start from the very basics.

Description

Become a Modern AI Engineer & Build Real-World AI Systems (GenAI + LLMs + Agents)

Unlock the power of Artificial Intelligence and Generative AI by learning how to build real-world, production-ready AI systems used in today’s industry.

The Problem

AI Engineers are in extremely high demand, but most learners struggle to break into this field because:

* AI is taught in disconnected topics (ML, DL, NLP, LLMs separately)

* Many courses focus only on theory or basic tools like ChatGPT

* There is no clear roadmap from beginner to advanced

* Building real-world AI applications feels overwhelming

Even after learning concepts, connecting everything into real systems is where most people get stuck.

The Solution

This course is designed as a complete, structured AI Engineer Bootcamp.

Instead of teaching isolated topics, this course takes you step-by-step through a clear roadmap:

Python → Machine Learning → Deep Learning → NLP → LLMs → RAG → AI Agents → Real Projects

You won’t just learn AI — you will build real AI systems.

What You Will Learn

Foundations of AI & Python

* Python for AI (NumPy, Pandas, Data Visualization)

* Data analysis and EDA (Exploratory Data Analysis)

* Core AI concepts and real-world applications

Machine Learning (Core)

* Regression, classification, clustering

* Model evaluation (accuracy, precision, recall)

* Overfitting vs underfitting

Projects:

* House Price Prediction

* Spam Email Classification

* Customer Segmentation

Deep Learning

* Neural networks and backpropagation

* CNNs for image data

* RNNs and LSTMs for sequences

* Introduction to Transformers

Natural Language Processing (NLP)

* Text preprocessing

* TF-IDF vs embeddings

* Word embeddings and BERT

Project:

* Sentiment Analysis System

Generative AI & LLMs

* Understanding Large Language Models (LLMs)

* Tokens and context windows

* GPT, Claude, LLaMA differences

* Open vs closed models

Transformers & Hugging Face

* Self-attention and transformer architecture

* Encoder vs decoder

* Using Hugging Face models and tokenizers

Prompt Engineering

* Zero-shot and few-shot prompting

* Chain-of-thought reasoning

* Prompt templates

Build Real AI Systems

Retrieval Augmented Generation (RAG)

* Chunking strategies

* Embeddings and similarity search

* Retrieval + generation pipelines

Project:

* PDF Question Answering System

AI Agents (LangChain & LangGraph)

* Tools, memory, and planning

* Single-agent and multi-agent workflows

Project:

* AI Research Agent

Bonus Topics

* Fine-tuning LLMs (LoRA, PEFT)

* Computer Vision basics

* Diffusion Models (Stable Diffusion)

* Build UI apps using Streamlit and Gradio

Hands-On Projects

This is a project-based course where you will build:

* EDA Notebook

* Machine Learning models

* NLP systems

* CNN image classifier

* RAG-based AI assistant

* LLM chatbot

* AI agent system

Who This Course Is For

* Developers who want to become AI Engineers

* DevOps / Cloud engineers moving into AI

* Students looking for a structured roadmap

* Anyone interested in Generative AI and LLMs

* Professionals who want hands-on AI skills

By the End of This Course

You will be able to:

* Build end-to-end AI applications

* Work with LLMs and modern AI tools

* Create AI agents and automation systems

* Design real-world AI solutions

* Apply for roles like AI Engineer, GenAI Engineer, and ML Engineer

What You Get

* Complete AI Engineer Bootcamp

* Hands-on real-world projects

* Lifetime access and future updates

* Certificate of completion

Final Note

AI is not the future — it’s already here.

The real question is:

Will you just use AI tools… or build them?

Start your journey today and become a job-ready AI Engineer.

Who this course is for:

Students who want to start a career as AI Engineers or Generative AI Engineers
Software developers and IT professionals who want to switch their career into AI and Generative AI
Beginners and non-technical learners curious about how modern AI systems like ChatGPT and LLMs work
Anyone who wants to build real AI projects like chatbots, RAG systems, and AI agents

The Complete AI & GenAI Engineer Bootcamp 2026: Zero to Hero

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 17min

Python for AI6 lectures • 4hr 8min

Math for AI3 lectures • 1hr 11min

Machine Learning9 lectures • 4hr 31min

Deep Learning with TensorFlow8 lectures • 4hr 13min

Natural Language Processing7 lectures • 3hr 16min

Computer Vision5 lectures • 1hr 50min

Large Language Model (LLM)4 lectures • 2hr 40min

Transformers Deep Dive3 lectures • 2hr 43min

Hugging Face5 lectures • 2hr 19min

Requirements

Description

Who this course is for: