# Kaggle Intro to Machine Learning Source: https://freeaicourses.com/courses/kaggle-intro-machine-learning Last checked: 2026-10-04 (hours, cost, instructor, lesson list and certificate wording) ## Short answer Our Kaggle Intro to Machine Learning review: it is the best free first machine learning course for people who already know basic Python. Dan Becker teaches 7 lessons in about 3 hours, from how models work to random forests and a machine learning competition. It costs nothing, and Kaggle gives a free completion certificate. ## Key facts - Instructor: Dan Becker (lesson 7 by Alexis Cook) - Time to complete: 3 hours (Kaggle's estimate) - Lessons: 7, with an exercise in lessons 2 to 7 - Cost: No cost - Builds on: Kaggle Learn: Python - Certificate: Free completion certificate - Prepares for: Machine Learning Explainability, Intermediate Machine Learning, Intro to Deep Learning ## Who it is for - Anyone who finished Kaggle Learn: Python and wants to build a first model - Learners with basic Python who want a short, free route into machine learning - People who want to try a machine learning competition, which the last lesson covers ## Who should skip it - Anyone who cannot yet write basic Python: take Kaggle Learn: Python first - Anyone wanting the theory behind the models first: try CS50 AI instead - Learners who want neural networks: Kaggle lists Intro to Deep Learning as a separate course ## What you can do afterwards - Explain in plain terms how a machine learning model works - Explore a dataset before you build anything on it - Build your first machine learning model - Check a model with validation, and tell underfitting from overfitting - Use a random forest and enter a machine learning competition ## What the course covers - How Models Work: A tutorial on how a machine learning model works. It is the only lesson without an exercise. - Basic Data Exploration: Looking at your data before you model it, with an exercise. - Your First Machine Learning Model: Building a first model, with an exercise. - Model Validation: Checking a model, with an exercise. - Underfitting and Overfitting: How a model can fit too little or too much, with an exercise. - Random Forests: A random forest model, with an exercise. - Machine Learning Competitions: Taking part in a machine learning competition, with an exercise. Alexis Cook wrote this lesson's notebook. ## What works - It costs nothing, and Kaggle says the same of all Kaggle Learn courses - Lessons 2 to 7 each have an exercise, so you practise as you go - About 3 hours by Kaggle's own estimate, short enough to finish in a weekend - Kaggle gives a free completion certificate for every course you finish - It builds on Kaggle Learn: Python and leads to Machine Learning Explainability, Intermediate Machine Learning and Intro to Deep Learning ## What does not - You need basic Python first, because Kaggle lists Python as the course it builds on - Lesson 1, How Models Work, is a tutorial only, with no exercise - Depth is limited: 3 hours end at random forests and a competition - Neural networks are not here: Kaggle lists Intro to Deep Learning as the next step for those - The certificate is a completion certificate, so it shows you finished rather than how well you did ## The catch Kaggle says this course builds on its Python course, so you need basic Python before you start. At about 3 hours it is an introduction: it ends at random forests and a competition, and Kaggle points you to Intermediate Machine Learning, Machine Learning Explainability and Intro to Deep Learning for what comes next. If 3 hours of lessons feel too light, Machine Learning Crash Course covers 12 modules and about 13.5 hours. ## Is it still current? Validation, overfitting and underfitting are core ideas that date slowly, so the lessons keep their value. Kaggle's course page was checked on 4 October 2026 and still lists all 7 lessons, from How Models Work to Machine Learning Competitions. ## Take before Kaggle Learn: Python ## Take after - Machine Learning Crash Course - CS50's Introduction to AI with Python ## Verdict Take Kaggle Intro to Machine Learning once you can write basic Python and want a first model quickly. It scores 8.3: hands-on is 9 because lessons 2 to 7 each have an exercise, and truly free is 10 because it costs nothing and the certificate is free, but depth is 6 because 3 hours end at random forests and a competition. If you want the theory first, take CS50 AI instead. ## Questions people ask ### Is Kaggle Intro to Machine Learning free? Yes. Kaggle lists the cost as no cost, like all Kaggle Learn courses, and the certificate is free too. ### Does Kaggle Intro to Machine Learning give a certificate? Yes. Kaggle says you earn a completion certificate for every course you finish. You find it under Courses, on the Completed tab, with View Certificate, and you can share it on LinkedIn or Twitter. ### How long does Kaggle Intro to Machine Learning take? Kaggle estimates 3 hours to earn the certificate, across 7 lessons. Lesson 1 is a tutorial only, and lessons 2 to 7 each have an exercise. ### Do I need Python for Kaggle Intro to Machine Learning? Yes. Kaggle lists its Python course as the one this builds on. If you cannot yet write basic Python, take Kaggle Learn: Python first. ### What should I take after Kaggle Intro to Machine Learning? Kaggle lists Machine Learning Explainability, Intermediate Machine Learning and Intro to Deep Learning as what this prepares you for. On this site, Machine Learning Crash Course is the next step: Google lists it at about 13.5 hours across 12 modules, so it is a much bigger commitment. CS50 AI is the alternative if you want more theory. ### Who teaches Kaggle Intro to Machine Learning? Dan Becker is the instructor listed on Kaggle's course page. The notebook for lesson 7, Machine Learning Competitions, is by Alexis Cook. The other lessons carry Dan Becker's name. ## Related pages - [Machine Learning Crash Course](https://freeaicourses.com/courses/machine-learning-crash-course) - [CS50's Introduction to AI with Python](https://freeaicourses.com/courses/cs50-ai) - [Kaggle Learn: Python](https://freeaicourses.com/courses/kaggle-python) - [Kaggle Learn](https://freeaicourses.com/platforms/kaggle) - [Python for AI, from zero](https://freeaicourses.com/paths/python-for-ai) - [Machine learning foundations](https://freeaicourses.com/paths/ml-foundations) - [Become an ML engineer](https://freeaicourses.com/paths/ml-engineer) - [From data analysis to AI](https://freeaicourses.com/paths/data-science-to-ai) - [All free AI courses, rated](https://freeaicourses.com/courses)