Kaggle Learn's Intermediate Machine Learning course teaches advanced techniques beyond intro ML: handling missing data, categorical variables, validation methods, and XGBoost (the most powerful traditional ML algorithm). You build real projects on actual Kaggle datasets. The course is completely free on Kaggle. After finishing, you understand production ML techniques and can handle real-world messy data. The main limitation: this is still below deep learning complexity. For true production systems, also study software engineering (testing, deployment).
XGBoost, taught in this course, has won over 65% of Kaggle competitions, making it the industry's most popular ML algorithm (source: Kaggle competitions analysis).
About This Course
Kaggle Learn's follow-up to Intro to ML. Covers missing values, categorical variables, pipelines, cross-validation, XGBoost, and data leakage. Four hours of focused, applied content.
Course Details
Platform
Kaggle LearnLanguage
machine-learningLevel
intermediate
Duration
4h
Certificate
Yes, included
Cost
Free
Frequently Asked Questions
Should I take this before deep learning?
Honestly, yes. Most real-world problems use traditional ML, not deep learning.
What's XGBoost?
An algorithm that outperforms neural networks on structured (tabular) data.
How is this different from Kaggle's intro ML course?
Intro teaches basic algorithms. Intermediate teaches the advanced techniques used in production.
Is this enough for a job?
As a foundation, yes. For a job, add domain knowledge (finance, healthcare, etc.) and real projects.
After this, what's next?
Deep learning, feature engineering, data engineering, or a specific domain.
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