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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%)
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Part of "NLP & Transformers" — Intermediate

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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"]

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  • ✓ PyTorch on Apple Silicon: MPS backend, Metal Performance Shaders
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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='...'
)
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🏆 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

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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)
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Intermediate

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# PyTorch neural network
import torch.nn as nn
model = nn.Sequential(
nn.Linear(784, 128), nn.ReLU()
)
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Text processing, BERT, HuggingFace, fine-tuning language models for real tasks.

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

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Getting Started

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

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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.

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02

Quantization Engineering

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

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03

Production Inference

High-throughput inference engines with vLLM and optimization strategies.

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