AI Luminaries
A curated collection of videos featuring top experts in artificial intelligence.
Overview
Master feature engineering with Kaggle datasets. Progress from baselines to advanced optimizations for Linear Regression, Random Forest, and LightGBM. Learn to diagnose model weaknesses and build weighted ensembles for peak predictive performance.
What you'll learn
- Explore and preprocess Kaggle datasets with pandas
- Build baseline regression models and establish performance benchmarks
- Diagnose model weaknesses through exploratory analysis and evaluation
- Engineer model-specific features for Linear Regression, Random Forest, and LightGBM
- Compare predictive performance using RMSE
- Refine feature pipelines based on experimental results
- Build weighted ensembles to improve predictive performance
Advantages and disadvantages
Advantages
- University courses you can audit for free, with lectures, readings and practice quizzes.
- A verified certificate from the university if you pay for it.
- Free, very short, with exercises in Kaggle notebooks.
- Free certificate for each micro-course.
- Self-paced: start any time.
Disadvantages
- Graded assignments and the certificate need the paid track.
- Audit access can expire a few weeks after the course ends.
- Covers the basics only.
- Learning is free, but the certificate costs money.
- Some parts (graded work, certificate) are paid.
Some points apply to every course of this kind; see how we rank.
Free to audit
- Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
- Paid: Graded assignments and the verified certificate (Certificate $45). Audit access may end after the course closes.
Before you start
Beginner: Data Cleaning and Preprocessing (Numpy, Pandas, Python, Scikit-learn, sklearn) Beginner: Programming and Algorithms (Numpy, Pandas, Python, Scikit-learn, sklearn)
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