ML demystified. From "what is machine learning" to training, evaluating, and deploying a real model — using scikit-learn, real datasets, and code that actually runs.
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from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) model = RandomForestClassifier(n_estimators=100) model.fit(X_train, y_train) print(classification_report(y_test, model.predict(X_test)))
No math proofs. Every concept is explained in terms of what problem it solves, with code you can run and results you can understand.
Before every algorithm, we explain what problem it solves. You'll understand the decision between logistic regression and a random forest before you run a line.
UCI ML Repository, Kaggle datasets, real tabular data. Not toy iris examples. You'll train models on data that looks like what you'll face in real projects.
Day 5 packages your model as a FastAPI endpoint. Not just a notebook — a running service that takes input and returns predictions. That's a production ML system.
Understand the three types of ML and when to use each. Learn the vocabulary: features, labels, training, inference. See the full ML workflow before writing any code.
Load a real dataset, split into train/test, train a logistic regression classifier, and evaluate it. Understand what the model learned and why it makes mistakes.
Build regression models. Train decision trees and visualize what they learned. Add randomness with Random Forests and watch accuracy improve.
Go beyond accuracy. Understand precision vs. recall. Use cross-validation for honest performance estimates. Detect and fix overfitting.
Build a scikit-learn pipeline. Save the model with joblib. Wrap it in a FastAPI endpoint. Deploy it and monitor for data drift.
All 5 days free. Forever. No paywall.
No spam. No credit card. Or go straight to Day 1.
Three days of intensive, hands-on AI training. Build production systems with real data, real APIs, and a cohort of peers. $1,490 all-in. Coming to 5 cities in October 2026.