Jupyter notebooks which may be found in this repo was part of Introduction to Machine learning coursera course by HSE (Higher School of Economics) University & Yandex School of Data Analysis.
I did not publish any data from the course itself except tasks conditions that are needed as a code context.
This course is nothing like legendary Andrew Ng's course in a way that Assignments are designed. You note just implement few lines of code, you're actually write everything. You may see most of the tasks as they were given to students (without theory, of course) in assignments itselves.
All code was written from scratch, except little hints like about import and model's parameters (this will be indicated in the task).
- Pandas basics, Decision trees
- k-nearest neighbor algorithm, Metric-tuning, Feature normalization
- SVM, logistic regression, Metrics of model performance quality
- Linear regression, Ridge regression
- Random forest, Gradient boosting, Neural networks