Intermediate Machine Learning

About this course

Kaggle's Intermediate Machine Learning course is your bridge between knowing the basics and building production-ready models. You'll tackle the real challenges that ML engineers face daily: messy data with missing values, categorical variables that need smart handling, and the subtle trap of data leakage that sinks otherwise-promising projects. This course cuts through theory to teach you the practical engineering skills that separate hobbyist notebooks from models that actually work in the real world.

What you'll learn

• Handle missing data strategically—decide when to drop, impute, or engineer features around gaps
• Transform categorical variables into formats your algorithms can use effectively
• Build reproducible machine learning pipelines that automate preprocessing and modeling
• Master cross-validation techniques to get honest estimates of how your model will perform on unseen data
• Train and tune XGBoost models—one of the most powerful algorithms for structured data competitions and real-world problems
• Recognize and eliminate data leakage, the silent killer that inflates accuracy during development but fails catastrophically in production
• Apply these skills to Kaggle datasets and build a portfolio piece that demonstrates professional-grade ML work

Who this is for

You're ready for this course if you've already learned the ML fundamentals—how supervised learning works, what regression and classification mean—and now want to stop losing competitions to overfitting and data mistakes. You should be comfortable with Python and pandas before starting.

• Data science students and fresh graduates—gain the intermediate skills that Indian tech companies expect for entry-level DS and ML engineering roles, standing out in campus placements and internship interviews
• Analytics professionals and business intelligence specialists—level up from SQL-based analytics to predictive modeling, opening doors to higher-paying data science tracks at your current or next company

Prerequisites

You should have completed a beginner ML course or have equivalent hands-on experience with Python, pandas, and scikit-learn. Familiarity with concepts like training/test splits and accuracy metrics is essential. No advanced mathematics required—the course focuses on practical implementation over theory.

Why this matters for Indian learners

Machine learning and data science are among India's fastest-growing tech skills, with companies across fintech (Paytm, PhonePe, Razorpay), e-commerce (Flipkart, Amazon India), and analytics (OYO, Unacademy) hiring aggressively for mid-level DS roles. These intermediate skills—especially XGBoost and pipeline automation—are precisely what separates junior analysts from data scientists commanding ₹8–15 lakh annually at Indian startups and ₹12–25 lakh at product companies. Building a Kaggle portfolio with this course gives you tangible proof of these skills when competing against thousands of other candidates in India's talent market.

Frequently asked questions

Is this course really free?

Yes, completely free. Kaggle Learn courses include video lessons, code notebooks, and exercises at no cost. You'll need a free Kaggle account to access it.

How long will it take to complete?

The course is structured for about 4 hours of focused work. If you're learning part-time, plan on 1–2 weeks at a comfortable pace, spending 30–45 minutes per lesson with time to experiment in the notebooks.

Will I get a certificate?

Yes. Kaggle issues a certificate of completion once you finish all lessons and exercises, which you can share on LinkedIn or include in your portfolio.