AIAcademy Enroll Now
The AI career accelerator

Master AI.
Shape the future.

Go from curious beginner to confident AI practitioner with hands-on courses in machine learning, deep learning, and generative AI — taught by engineers who ship real products.

Start Learning Explore Courses
24k+
Students enrolled
4.9/5
Average rating
120h
Project-based content
Featured Courses

Start with a bestseller

View all courses
Laptop screen filled with Python machine learning code Beginner
★ 4.9·12 weeks·8,400 students

Machine Learning Foundations

Build core ML intuition with Python — regression, classification, and your first trained model.

Course details
Engineer studying deep neural network visualizations on screen Intermediate
★ 4.8·10 weeks·5,100 students

Deep Learning with PyTorch

Design and train neural networks from scratch — CNNs, RNNs, and transfer learning in practice.

Course details
Humanoid robot hand representing generative AI technology Advanced
★ 4.9·8 weeks·3,700 students

Generative AI & LLM Engineering

Fine-tune transformers, build RAG pipelines, and ship production-ready LLM applications.

Course details
Your Learning Path

Three steps to AI fluency

A guided sequence that takes you from fundamentals to building production AI systems — at your own pace.

  1. 1

    Learn the Foundations

    Master Python, math for ML, and core algorithms through interactive notebooks and weekly exercises.

    4–6 weeks · 8 modules
  2. 2

    Build Real Projects

    Train models on real datasets, ship an end-to-end ML app, and grow a portfolio recruiters notice.

    6–8 weeks · 5 portfolio projects
  3. 3

    Specialize & Get Hired

    Choose a specialization, complete a capstone with mentor review, and prep for AI role interviews.

    4 weeks · Career support included
Meet Your Instructor

Learn from someone who ships AI

Portrait of Dr. Daniel Reyes, lead AI instructor
Former ML lead — 12 years in applied AI

Dr. Daniel Reyes

Lead Instructor · Applied AI Engineer

Daniel spent a decade building recommendation and vision systems at scale before turning to teaching full-time. His courses focus on the 20% of theory that powers 80% of real-world results.

He's mentored over 4,000 engineers into AI roles — and every lesson is built around code you can actually run, break, and rebuild.

  • PhD in Computer Science, Carnegie Mellon
  • 12+ years shipping production ML systems
  • 4,000+ students mentored into AI careers

"You don't need a genius-level math background to work in AI. You need curiosity, consistent practice, and a guide who's done it before. That's exactly what this course gives you."

— Dr. Daniel Reyes
Course Curriculum

What you'll learn, module by module

01 Foundations of Machine Learning 6 lessons · 9 hours · Python, NumPy & core algorithms

Set up your environment and build the mental models behind every ML system: features, loss functions, overfitting, and evaluation.

  • · Linear & logistic regression from scratch
  • · Train/test splits and cross-validation
  • · Gradient descent, visualized
  • · Mini-project: predict housing prices
02 Deep Learning & Neural Networks 8 lessons · 12 hours · PyTorch, CNNs & transfer learning

Move from shallow models to deep networks. Build, train, and debug neural architectures on real image and text datasets.

  • · Building networks with PyTorch
  • · CNNs and image classification
  • · Transfer learning with pretrained models
  • · Mini-project: custom vision classifier
03 Generative AI & LLM Engineering 7 lessons · 10 hours · Transformers, RAG & deployment

Work with the technology defining the field right now: transformers, fine-tuning, retrieval-augmented generation, and shipping LLM apps safely.

  • · Transformer architecture, demystified
  • · Fine-tuning & prompt engineering
  • · RAG pipelines with vector databases
  • · Capstone: production AI chat assistant