Data and AI

Learn Machine Learning

3 plans — 19 lessons

Supervised models, evaluation methods, and feature basics.

Beginner Path14-day sprint

14-day sprint

Learn Machine Learning in 14 days

Supervised models, evaluation methods, and feature basics.

Machine learning concepts and terminology
Data prep and feature engineering
Supervised models and training
+3 more topics

Start plan

Intermediate Path21-day sprint

21-day sprint

Intermediate Machine Learning in 21 days

Feature engineering, ensemble methods, XGBoost, hyperparameter tuning, and sklearn pipelines.

Feature engineering and selection
Model evaluation and cross-validation
Ensemble methods: bagging and boosting
+4 more topics

Start plan

Advanced Path30-day sprint

30-day sprint

Advanced Machine Learning in 30 days

Deep learning with PyTorch, CNNs, model deployment with FastAPI, and MLOps for production.

Deep learning foundations with PyTorch
CNNs and transfer learning
NLP and transformer models
+3 more topics

Start plan

What You'll Learn

01Foundation

Machine learning concepts and terminology

Understand models, features, labels, and datasets.

conceptsmodelsbasics

02Foundation

Data prep and feature engineering

Prepare data and create useful features.

featurespreprocessingdata

03Core

Supervised models and training

Train models like regression and classification.

trainingsupervisedmodels

04Core

Evaluation and validation

Measure accuracy and avoid data leakage.

evaluationvalidationmetrics

05Core

Overfitting and model tuning

Tune hyperparameters and reduce overfitting.

tuningoverfittingoptimization

06Challenge

Mini project: predictive model

Build a simple predictive model end-to-end.

projectpredictionpractice

Build models that actually work

14 days of supervised learning, feature engineering, and model evaluation — end with a real predictive model.

Start learning

Kavka's ML plan skips the hype and focuses on the fundamentals. I finally understand how to evaluate a model.

— Alex N., ML engineer

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