Machine LearningBeginner
Machine Learning Crash Course
Google's fast-paced introduction to machine learning. Covers ML concepts, TensorFlow APIs, and real-world case studies. Written and maintained by Google engineers. Completely free.
Free course on Kaggle Learn
Intermediate Machine Learning (Kaggle)
4 hours 4.8(41,000 reviews)Free
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 was used in 17 of the 29 challenge-winning solutions posted on Kaggle's blog in 2015 (source: Chen and Guestrin, "XGBoost: A Scalable Tree Boosting System," KDD 2016).
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 Learn
- Language
- Machine Learning
- Level
- 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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