Free to join · Sign in with Google

Go from Zero to
AI Professional

Hands-on AI education for every level. Start with Git and Python, build your first AI model, then master LLMs, RAG, and production deployment.

⚡ Try Now

Build a Sentiment Classifier

See what you'll learn. Sign in to run this code interactively.

# Classify text sentiment with 3 lines of code
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
result = classifier("I love AI!")
# Output: POSITIVE (99.9%)
Sign In to Run Code

Part of "Getting Started" — 30 min

50+
Courses
769
Modules
1,712
Quiz Questions
100%
Free & Open
⚙️ Your Competitive Edge

Learn to Deploy, Not Just Build

Most AI courses teach theory. We teach infrastructure. Master the production pipeline: experiment tracking, model versioning, cloud orchestration, and automated deployment — the skills that separate hobbyists from production engineers.

Build locally on Apple Silicon, scale to AWS, or deploy private LLMs on-premise. Learn the full MLOps lifecycle.

  • ✓ Experiment Tracking & Versioning: MLflow, DVC, model registries
  • ✓ CI/CD for ML: Automated testing, retraining pipelines, deployment automation
  • ✓ Cloud Orchestration: AWS SageMaker, Kubernetes, serverless inference
  • ✓ Local & Edge Deployment: Quantization, Apple Silicon optimization, private LLMs
Explore MLOps Courses →
mlflow_tracking.py
# Track model experiments automatically
import mlflow
mlflow.start_run()
mlflow.log_param("lr", 0.001)
mlflow.log_metric("accuracy", 0.95)
mlflow.end_run()
Dockerfile
# Deploy model as containerized service
FROM python:3.11
COPY model.pkl /app/
RUN pip install -r requirements.txt
CMD ["python", "serve.py"]

Everything you need to master AI

Learn, practice, and get certified — all for free.

48 Courses

From Python basics to production AI. Structured paths with quizzes and progress tracking.

AI Tutor

CodeMentor knows what you're studying and gives context-aware answers — 24/7.

Run Code in Browser

Execute Python directly in your browser or open in Google Colab — zero setup needed.

Earn Certificates

Complete a course, pass the quizzes, and download a shareable certificate.

Progress Tracking

Your progress syncs across all devices automatically.

Study Clubs

Learn with friends. Create a club and track each other's progress.

Visual Diagrams

Custom diagrams and illustrations to help you understand complex concepts.

Production Ready

Learn RAG, agents, MLOps, and deployment — not just theory.

💻 Train Locally, Deploy Globally

Stop Paying for Cloud Experiments

Train models on your local machine using Apple Silicon, maximize GPU memory, and iterate at lightning speed — without cloud bills.

Most platforms assume cloud-first. We teach you to develop locally, then deploy globally when you're ready.

  • ✓ PyTorch on Apple Silicon: MPS backend, Metal Performance Shaders
  • ✓ Local LLM Deployment: Ollama, llama.cpp, private inference
  • ✓ Memory Optimization: Quantization, pruning, efficient inference
  • ✓ Local Pipelines: DVC, Git, Docker — before pushing to production
Explore Local Development Courses →
pytorch_mps.py
# Train on Apple Silicon GPU
import torch
device = torch.device("mps")
model = model.to(device)
output = model(input.to(device))
local_llm.py
# Run LLM locally, no API calls
from ollama import Client
client = Client(host='localhost')
response = client.generate(
model='llama2', prompt='...'
)
See It in Action

Real Code. Real Results.

From sentiment analysis to RAG systems — see the exact code you'll learn.

💬

Sentiment Analysis

# 3 lines to classify text
from transformers import pipeline
clf = pipeline("sentiment")
clf("I love this!")
Learn NLP
📚

RAG System

# Query your documents
from langchain import RAG
rag = RAG.from_docs(docs)
rag.query("What is MLOps?")
Learn RAG
🤖

AI Agent

# Agent that reasons & acts
from crewai import Agent
agent = Agent(tools=[...])
agent.run("Analyze this data")
Learn Agents
Test Your Knowledge

Daily AI Quiz

20 random questions from our bank of 1,712 — drawn from all courses and difficulty levels.

🏆 This Week's Challenge

Build a Sentiment Classifier

Take 30 minutes to build a real AI model that classifies text sentiment. Use HuggingFace transformers and test it on your own examples.

✓ Learn: How transformers work for text classification
✓ Build: A working sentiment classifier in 10 lines of code
✓ Deploy: Test on real text and share your results
Start Challenge View All Challenges →

⏱️ 30 min • 🎯 Beginner • 📊 NLP & Transformers

Challenge Stats
247
Completed This Week
4.8★
Average Rating
Recent Completions
A
Alex completed in 28 min ✓
J
Jordan completed in 35 min ✓
S
Sarah completed in 31 min ✓
New to AI?

Start Here

No experience needed. Set up your environment, learn the tools every AI engineer uses, and write your first AI script in one session.

Git & GitHub

Version control every AI engineer needs. Clone, commit, push.

Start →

Python Setup

Install Python, create virtual environments, manage packages.

Start →

Dev Tools

VS Code, Jupyter, Conda — the AI developer's toolkit.

Start →

First AI Script

Build a working sentiment classifier in under 30 minutes.

Start →
Begin the Getting Started Track →
Core Curriculum

AI & Machine Learning Courses

Structured learning from fundamentals to advanced topics. All content requires sign-in to track your progress.

View full catalog
Beginner

AI Fundamentals

Core ML concepts, Python for data science, supervised vs unsupervised learning, your first scikit-learn model.

# Your first ML model
from sklearn.ensemble import RandomForest
model = RandomForest().fit(X, y)
24 modules Preview →
Intermediate

Deep Learning

Neural networks, PyTorch, CNNs, training on GPU. Build and train models from scratch.

# PyTorch neural network
import torch.nn as nn
model = nn.Sequential(
nn.Linear(784, 128), nn.ReLU()
)
25 modules Preview →
Intermediate

NLP & Transformers

Text processing, BERT, HuggingFace, fine-tuning language models for real tasks.

25 modules Sign-in required
Intermediate

Computer Vision

Image classification, object detection, transfer learning with ResNet and EfficientNet.

20 modules Sign-in required
Advanced

MLOps Guide

CI/CD for ML, model versioning, monitoring, and deploying models to production.

24 modules Sign-in required
Start Here

Getting Started

Git, Python, VS Code, Jupyter — everything you need before writing your first AI model.

21 modules Sign-in required
Pro Series

Pro Series: Advanced Engineering

Deep-dive guides for engineers building production AI systems at scale.

01

Architecture Selection

Choosing model architectures for enterprise privacy and local deployment.

Read Guide →
02

Quantization Engineering

Mastering GGUF, AWQ, and model compression for edge deployment.

Read Guide →
03

Production Inference

High-throughput inference engines with vLLM and optimization strategies.

Read Guide →
Community

Join the AI Learning Club

Connect with fellow learners, share projects, ask questions, and get help from the community. Free to join.

Ready to start?

Sign in with Google — it's free. Your progress is saved automatically.

Google Continue with Google