intermediateCertificateFree
Intermediate Machine Learning
by Alexis Cook · Kaggle
4.5(7,200 reviews)
400K+ enrolled4 hoursUpdated 2024-03
What You'll Learn
Understand the fundamentals and key concepts of machine learning
Apply machine learning techniques to solve real-world problems
Understand the fundamentals and key concepts of XGBoost
Apply XGBoost techniques to solve real-world problems
Understand the fundamentals and key concepts of cross-validation
Apply cross-validation techniques to solve real-world problems
About This Course
Handle missing values, categorical variables, pipelines, cross-validation, XGBoost, and data leakage in ML workflows.
Curriculum
Module 1: Machine learning & XGBoost6 lessons
- Introduction to machine learning
- Machine learning in Practice
- Hands-on Exercise: Machine learning
- Introduction to XGBoost
- XGBoost in Practice
- Hands-on Exercise: XGBoost
Module 2: Cross-validation & Feature engineering6 lessons
- Introduction to cross-validation
- Cross-validation in Practice
- Hands-on Exercise: Cross-validation
- Introduction to feature engineering
- Feature engineering in Practice
- Hands-on Exercise: Feature engineering
Module 3: Scikit-learn3 lessons
- Introduction to scikit-learn
- Scikit-learn in Practice
- Hands-on Exercise: Scikit-learn
Instructor
Alexis Cook
Instructor at Kaggle
Pros & Cons
Pros
- Completely free to access
- Offers a certificate of completion
- Hands-on interactive exercises
- Taught by Alexis Cook
Cons
- Self-paced requires discipline
Free
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