Intermediate Machine Learning in 21 days
Feature engineering, ensemble methods, XGBoost, hyperparameter tuning, and sklearn pipelines.
- Daily plan, 45-60 min a day
- 7 lessons + 21 exercises
- AI tutor included
- No prior coding experience required
- Practice tasks every day
- Build impressive portfolio project
What you will learn
A powered by mentors Intermediate plan with structured subtopics, quizzes, and practice tasks.
Feature engineering and selection
Create informative features and remove noise for better model performance.
Model evaluation and cross-validation
Use k-fold CV, ROC-AUC, precision/recall, and confusion matrices.
Ensemble methods: bagging and boosting
Improve predictions with Random Forests, AdaBoost, and stacking.
Gradient boosting with XGBoost and LightGBM
Win Kaggle-style competitions with gradient boosting frameworks.
Hyperparameter tuning
Automate search with GridSearchCV, RandomizedSearch, and Optuna.
sklearn pipelines and transformers
Chain preprocessing and modelling steps into reproducible pipelines.
Capstone: Kaggle-style classification challenge
Apply the full ML workflow to a real structured dataset.
See the quiz + practice flow
Three answered questions and a filled code task so you know exactly what to expect.
Quiz preview
2/3 correct1. How long is the Machine Learning plan?
Correct2. What level is this Machine Learning plan?
Incorrect3. Which of these appears in the Machine Learning outline?
CorrectCode practice preview
SubmittedFormat a lesson title
Build a helper that formats a lesson label with a padded index and title.
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