mlcourse.ai is an open Machine Learning course by OpenDataScience. The course is designed to perfectly balance theory and practice; therefore, each topic is followed by an assignment with a deadline in a week. You can also take part in several Kaggle Inclass competitions held during the course and work on your own projects.
Next session starts on October 1, 2018. Fill in this form to participate.
Navigation:
- Prerequisites. Our course is not for total newbies. Though Machine Learning is covered from scratch, still participants are expected to know some math and be able to write code in Python.
- Assignments. Here you’ll find demo versions of assignments. Assignments in a new session of the course will be different.
- News. Here you can track main announcements during the course.
- Resources. Links to other information mirrors of this course like Medium stories, Kaggle Kernels etc.
- Contacts. Ways of reaching OpenDataScience and course team.
- Support. Various ways in which you can help mlcourse.ai to grow.
Topic 10. Gradient Boosting
Topic 9. Time series analysis in Python. Part 2. Predicting future with Facebook Prophet
Topic 9. Time series analysis in Python. Part 1. Basics
Topic 8. Vowpal Wabbit: Learning with Gigabytes of Data
Topic 7. Unsupervised learning
Topic 6. Feature engineering and feature selection
Topic 5. Ensembles of algorithms and random forest. Part 3. Feature importance
Topic 5. Ensembles of algorithms and random forest. Part 2. Random Forest
Topic 5. Ensembles of algorithms and random forest. Part 1. Bagging
Topic 4. Linear Classification and Regression. Part 5. Validation and learning curves
Topic 4. Linear Classification and Regression. Part 4. Pros and Cons
Topic 4. Linear Classification and Regression. Part 3. Regularization
Topic 4. Linear Classification and Regression. Part 2. Logistic Regression
Topic 4. Linear Classification and Regression. Part 1. Ordinary Least Squares
Topic 3. Classification, Decision Trees and k Nearest Neighbors
Topic 2. Overview of Seaborn, Matplotlib and Plotly libraries
Topic 2. Visual data analysis in Python
Topic 1. Exploratory data analysis with Pandas