yorko/mlcourse.ai

★ 10,589PythonAudience · dataComplexity · 2/5LicenseSetup · easy

Mindmap

mindmap
  root((mlcourse.ai))
    What it does
      Free ML course
      10 week curriculum
      Self-paced
    Topics covered
      Classification
      Gradient boosting
      Time series
      Neural networks
    Use cases
      Kaggle competitions
      Self-study ML
      Teaching material
    Tech stack
      Python
      Jupyter notebooks
      Pandas

mindmap root((mlcourse.ai)) What it does Free ML course 10 week curriculum Self-paced Topics covered Classification Gradient boosting Time series Neural networks Use cases Kaggle competitions Self-study ML Teaching material Tech stack Python Jupyter notebooks Pandas

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Things people build with this

USE CASE 1

Work through a structured 10-week ML curriculum with hands-on Kaggle competitions to build practical skills.

USE CASE 2

Study Jupyter notebooks covering decision trees, gradient boosting, PCA, and time series with real datasets.

USE CASE 3

Adapt the course materials to teach or self-study machine learning concepts in English, Russian, or Chinese.

USE CASE 4

Implement algorithms like stochastic gradient descent and gradient boosting from scratch using the bonus assignments.

Tech stack

PythonJupyterPandasScikit-learnNumPy

Getting it running

Difficulty · easy Time to first run · 30min

Requires a Python environment with Jupyter, scikit-learn, pandas, and matplotlib installed to run the course notebooks.

Free to use and share for non-commercial purposes only, you must give credit and share any adaptations under the same Creative Commons NonCommercial-ShareAlike terms.

In plain English

mlcourse.ai is a free, open machine learning course produced by the OpenDataScience community. It was created by Yury Kashnitsky, who holds a Ph.D. in applied mathematics and has reached the Kaggle Competitions Master tier. The course is structured as 10 weeks of material and is currently available in self-paced mode, meaning you work through it at your own speed without fixed deadlines. The curriculum covers the main areas of practical machine learning: exploratory data analysis with Pandas, data visualization, classification with decision trees and nearest neighbors, linear models for classification and regression, ensemble methods like random forests and gradient boosting, feature engineering, dimensionality reduction with PCA, clustering, time series analysis, and neural networks. Each topic comes with a written article and a Jupyter notebook with exercises. Articles are available in English and Russian, and translated notebooks in Chinese are also linked. Assignments are part of the course experience. Several of the topics include in-class Kaggle competitions where you apply what you learned to a real prediction problem and compete against other students. The course emphasizes both the mathematical foundations and hands-on practice, with the goal of giving students a working understanding rather than just a surface-level introduction. There is an optional Bonus Assignments pack available for a monthly contribution of $17 through Patreon. These are extended, non-demo versions of certain assignments, including challenges to beat a Kaggle baseline and tasks to implement algorithms like stochastic gradient descent and gradient boosting from scratch. Unlike the main course, the bonus pack is copyrighted. The course content is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0, making it free to use and share for non-commercial purposes. The repository contains the Jupyter notebooks, course materials, and the configuration for building the course website.

Copy-paste prompts

Prompt 1

Using the mlcourse.ai gradient boosting notebook as a reference, help me tune XGBoost hyperparameters for a Kaggle tabular data competition.

Prompt 2

I am working through mlcourse.ai week 3 on decision trees. Explain information gain and Gini impurity using the notebook examples as context.

Prompt 3

Generate a study plan for completing the mlcourse.ai 10-week curriculum while working 20 hours per week, with suggested Kaggle practice tasks for each topic.

Prompt 4

Using mlcourse.ai feature engineering techniques, help me build features for a time series sales prediction task from raw transaction data.

Prompt 5

Help me implement stochastic gradient descent from scratch following the mlcourse.ai bonus assignment approach, with a test on the iris dataset.

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