This course can be taken independent of, concurrent with, or prior to, its companion course, Mindshift. If you’ve ever wanted to become better at anything, this course will help serve as your guide. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’re already an expert, this peep under the mental hood will give you ideas for turbocharging successful learning, including counter-intuitive test-taking tips and insights that will help you make the best use of your time on homework and problem sets. Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life.

We’ll also cover illusions of learning, memory techniques, dealing with procrastination, and best practices shown by research to be most effective in helping you master tough subjects. We’ll learn about how the brain uses two very different learning modes and how it encapsulates (“chunks”) information. Some courses have notebooks in a subdirectory of the learntools package, reflecting the fact these notebooks were authored and edited outside our templating system.This course gives you easy access to the invaluable learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines.

The README in /notebooks has instructions to convert raw notebooks to rendered notebooks (and to use the templating system more generally). The notebooks are processed in a templating system before being uploaded to kaggle, so the raw notebooks are hard to read. Replace python with another course name to find the materials for other courses. The notebooks for the python course are in /notebooks/python/raw/*.

The notebooks subdirectory contains tools to simplify publishing courses on kaggle as well as the course materials themselves.

This is imported into the modules for each course.

core provides the infrastructure for exercise checking.

learntools/machine_learning is used to check exercises in the Machine Learning course. For example, learntools/python is used to check exercises in the Python course.

The learntools folder contains a python package that provides feedback to users in Kaggle Learn courses.

This repo is split into two types of material.

The checking code and notebooks used in Kaggle Learn courses.Įverything here is open source, but these materials haven't been designed to work independently and likely aren't useful outside of Kaggle Learn.

How to learn intools