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Complete AI Course — Artificial Intelligence from Fundamentals to Production [2026]

Master Artificial Intelligence from the ground up. Learn neural networks, deep learning with PyTorch, CNNs, NLP, Transformers, LLMs, fine-tuning, generative AI, reinforcement learning, AI deployment, and responsible AI — with real-world projects in every module.

12
Modules
36
Hours
96
Topics
12
Projects
🎯

Who This Course Is For

For developers, researchers, and product managers entering the AI field. Covers core concepts behind ChatGPT, image recognition, and recommendation systems — without unnecessary academic jargon.

📋

Prerequisites

Python fundamentals and basic mathematics (algebra, probability concepts).

First published June 2024 · Updated 2026

What You'll Learn

  • AI fundamentals: neural networks, deep learning, and PyTorch
  • Computer vision with CNNs and Vision Transformers
  • NLP with RNNs, BERT, and Transformer architectures
  • Large Language Models: GPT, RLHF, prompt engineering, and RAG
  • Fine-tuning LLMs with LoRA, QLoRA, and HuggingFace
  • Generative AI: diffusion models, GANs, and multimodal AI
  • Reinforcement learning and autonomous AI agents
  • Production AI deployment with FastAPI, Docker, and MLOps

Career Opportunities

AI Engineer
Machine Learning Engineer
Deep Learning Engineer
NLP Engineer
AI Research Scientist
MLOps Engineer
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Course Modules Overview

1
AI Foundations & Modern Landscape

9 topics

2
Neural Networks & Deep Learning Fundamentals

9 topics

3
PyTorch — Practical Deep Learning

8 topics

4
Computer Vision & CNNs

7 topics

5
Natural Language Processing (NLP)

8 topics

6
Transformer Architecture & Attention

9 topics

7
Large Language Models (LLMs)

8 topics

8
Fine-Tuning LLMs & HuggingFace Ecosystem

8 topics

9
Generative AI — Diffusion, GANs & Multimodal

8 topics

10
Reinforcement Learning & AI Agents

7 topics

11
AI Deployment & Production Systems

7 topics

12
AI Ethics, Safety & Responsible AI

8 topics

Keep Learning!

Complete all 12 modules to unlock your course completion certificate

Course Curriculum

12 comprehensive modules covering everything from basics to advanced topics

Beginner (3)Intermediate (4)Advanced (5)
Module 1

AI Foundations & Modern Landscape

Understand what AI really is: the difference between AI, ML and Deep Learning, narrow vs general AI, key milestones, and the modern AI ecosystem.

Beginner2.5 hours
AI vs ML vs Deep Learning — The Hierarchy ExplainedNarrow AI vs General AI vs SuperintelligenceAI History — From Expert Systems to Foundation ModelsAI System Design — Data → Model → Inference → FeedbackAI Benchmarks — How We Measure AI ProgressThe Modern AI Ecosystem — Tools & PlayersTypes of AI Tasks — Classification, Generation, RetrievalSetting Up Your AI Development EnvironmentAI Career Paths & What Comes Next
Start Module
Module 2

Neural Networks & Deep Learning Fundamentals

Build neural networks from scratch: perceptrons, forward propagation, backpropagation, optimizers, regularization, and a complete MNIST project.

Beginner3 hours
The Perceptron — Building Block of All Neural NetworksMulti-Layer Networks & Forward PropagationActivation Functions — Why Networks Are Non-LinearLoss Functions — Measuring Model ErrorBackpropagation — How Networks LearnOptimizers — SGD, Adam, AdamW & Learning Rate SchedulersRegularization — Preventing OverfittingTraining Loop — Complete ImplementationMini Project: Train an MLP on MNIST
Start Module
Module 3

PyTorch — Practical Deep Learning

Master PyTorch: tensors, nn.Module, DataLoaders, GPU training, transfer learning, experiment tracking, and build a Dog vs Cat classifier.

Beginner3 hours
PyTorch Tensors — The Fundamental Data Structurenn.Module — Building Custom ModelsDataset & DataLoader — Efficient Data PipelinesGPU Acceleration & Mixed Precision TrainingModel Saving, Loading & CheckpointingTransfer Learning — Reuse Pre-trained ModelsTensorBoard & W&B — Experiment TrackingMini Project: Dog vs Cat Classifier with Transfer Learning
Start Module
Module 4

Computer Vision & CNNs

Master computer vision: convolution, CNN architectures, data augmentation, YOLO, segmentation, Vision Transformers, and build a CIFAR-10 classifier.

Intermediate3 hours
Convolution — The Core Operation of Computer VisionCNN Architectures — LeNet to EfficientNetData Augmentation — Getting More from Less DataObject Detection — YOLO ConceptsImage Segmentation — Pixel-Level UnderstandingVision Transformers (ViT) — Transformers for ImagesMini Project: CIFAR-10 Image Classifier
Start Module
Module 5

Natural Language Processing (NLP)

Master NLP: tokenization, embeddings, RNNs, BERT, NER, seq2seq, attention mechanism, and build an IMDB sentiment analysis system.

Intermediate3 hours
Tokenization — Converting Text to NumbersWord Embeddings — Semantic RepresentationsRNN, LSTM & GRU — Sequence ModelsText Classification — Sentiment Analysis with BERTNamed Entity Recognition (NER)Sequence-to-Sequence — Translation & SummarizationAttention Mechanism — The Precursor to TransformersMini Project: Sentiment Analysis — IMDB with BiLSTM + BERT Comparison
Start Module
Module 6

Transformer Architecture & Attention

Deep dive into Transformers: self-attention, multi-head attention, positional encoding, BERT/GPT/T5 architectures, scaling laws, and FlashAttention.

Intermediate3.5 hours
Self-Attention — QKV Computation from ScratchMulti-Head Attention — Parallel Attention SubspacesPositional Encoding — Giving Transformers OrderFull Transformer Block — Encoder and DecoderBERT vs GPT vs T5 — Architecture ChoicesScaling Laws — Why Bigger Models Work BetterFlashAttention — Efficient Attention for Long ContextsBuild a Mini Transformer for Text ClassificationCross-Attention & Encoder-Decoder Communication
Start Module
Module 7

Large Language Models (LLMs)

Master LLMs: pretraining, RLHF, prompt engineering, RAG, OpenAI API, LangChain, evaluation, and build a production RAG customer support bot.

Intermediate3 hours
How LLMs Work — Pretraining & Autoregressive GenerationRLHF — Training LLMs to Follow InstructionsPrompt Engineering — Getting the Best from LLMsRAG — Retrieval-Augmented GenerationOpenAI API — GPT-4o for Production ApplicationsLangChain — Building LLM ApplicationsLLM Evaluation — Measuring Output QualityMini Project: AI Customer Support Bot with RAG
Start Module
Module 8

Fine-Tuning LLMs & HuggingFace Ecosystem

Master LLM fine-tuning: LoRA, QLoRA, chat templates, SFTTrainer, quantization, HuggingFace Hub, multi-GPU training, and fine-tune Llama 3.

Advanced3 hours
Fine-Tuning Strategies — Full, LoRA, and QLoRAChat Templates — Formatting Data for Instruction TuningSFTTrainer — HuggingFace Fine-Tuning PipelineQuantization & GGUF — Running LLMs on CPUHuggingFace Ecosystem — Datasets, Hub, SpacesMulti-GPU Training & DeepSpeed ZeROLLM Evaluation — Benchmarking Fine-Tuned ModelsMini Project: Fine-Tune Llama 3 for Code Review
Start Module
Module 9

Generative AI — Diffusion, GANs & Multimodal

Master generative AI: diffusion models, Stable Diffusion, ControlNet, GANs, CLIP, text-to-video, multimodal AI, and build an image generation app.

Advanced3 hours
Diffusion Models — How They WorkStable Diffusion Architecture — U-Net + CLIP + VAEControlNet — Precise Image ControlGANs — Generative Adversarial NetworksCLIP — Connecting Language and VisionText-to-Video — Sora Architecture ConceptsMultimodal AI — Vision-Language ModelsMini Project: AI Image Generation App
Start Module
Module 10

Reinforcement Learning & AI Agents

Master RL and AI agents: MDPs, DQN, PPO, LLM agents, multi-agent systems, agentic memory, and build an autonomous research agent.

Advanced3 hours
RL Foundations — MDP, Rewards, and PoliciesDeep Q-Network (DQN) — Atari with Neural NetworksPPO — Proximal Policy OptimizationLLM-Powered AI Agents — Tool Use and PlanningMulti-Agent Systems — Agents CollaboratingAgentic Memory — Persistent Context ManagementMini Project: Autonomous Research Agent
Start Module
Module 11

AI Deployment & Production Systems

Deploy AI to production: FastAPI serving, ONNX, Docker/K8s, model monitoring, MLOps CI/CD, vLLM serving, and build a production ML API.

Advanced3 hours
FastAPI Model Serving — Production REST APIONNX Export — Accelerated InferenceDocker & Kubernetes — Containerized AI ServicesModel Monitoring — Detecting Drift in ProductionMLOps — CI/CD for Machine LearningOptimized LLM Serving — vLLM and TGIMini Project: Production ML API with Monitoring
Start Module
Module 12

AI Ethics, Safety & Responsible AI

Master responsible AI: bias detection, LLM safety, SHAP/LIME, privacy, EU AI Act, alignment, Constitutional AI, and build an AI audit system.

Advanced3 hours
AI Bias — Detection, Measurement, and MitigationLLM Safety — Alignment, Red-Teaming, and GuardrailsModel Interpretability — SHAP and LIMEPrivacy-Preserving AI — Differential Privacy & Federated LearningAI Regulation — EU AI Act and Global FrameworksAI Safety — Alignment and Long-Term RiskConstitutional AI — Anthropic's Safety FrameworkCapstone Project: Responsible AI Audit System
Start Module

Your Learning Roadmap

Follow this structured path — from first concepts to production-ready mastery

🌱
Step 01 · Beginner

AI foundations, neural networks, PyTorch, and deep learning fundamentals

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Step 02 · Intermediate

Computer vision, NLP, Transformers, LLMs, and RAG applications

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Step 03 · Advanced

Fine-tuning, generative AI, RL agents, deployment, and responsible AI

Tools & Technologies

Essential tools you'll master during this course

PyTorch

Deep learning research & production framework

Framework

HuggingFace

Pre-trained models, datasets, and Spaces

Model Hub

OpenAI API

GPT-4o, embeddings, and fine-tuning

LLM API

LangChain

Chains, agents, memory, and RAG

LLM Framework

FastAPI

High-performance Python API framework

Model Serving

Docker

Application containerization for AI

Containerization

Ready to Start Learning?

Begin your journey with Module 1 and build your skills step by step. Completely free, no registration required.

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