mlcourse.ai, open Machine Learning course
Mirrors (
Outline
This is the list of published articles on medium.com
- Exploratory Data Analysis with Pandas
π¬π§ π·πΊ π¨π³ , Kaggle Kernel - Visual Data Analysis with Python
π¬π§ π·πΊ π¨π³ , Kaggle Kernels: part1, part2 - Classification, Decision Trees and k Nearest Neighbors
π¬π§ π·πΊ π¨π³ , Kaggle Kernel - Linear Classification and Regression
π¬π§ π·πΊ π¨π³ , Kaggle Kernels: part1, part2, part3, part4, part5 - Bagging and Random Forest
π¬π§ π·πΊ π¨π³ , Kaggle Kernels: part1, part2, part3 - Feature Engineering and Feature Selection
π¬π§ π·πΊ π¨π³ , Kaggle Kernel - Unsupervised Learning: Principal Component Analysis and Clustering
π¬π§ π·πΊ π¨π³ , Kaggle Kernel - Vowpal Wabbit: Learning with Gigabytes of Data
π¬π§ π·πΊ π¨π³ , Kaggle Kernel - Time Series Analysis with Python, part 1
π¬π§ π·πΊ π¨π³ . Predicting future with Facebook Prophet, part 2π¬π§ , Kaggle Kernels: part1, part2 - Gradient Boosting
π¬π§ π·πΊ , Kaggle Kernel
Assignments
- Exploratory data analysis of Olympic games with Pandas, nbviewer. Deadline: October 14, 20:59 CET
Demo assignments, just for practice, not to be accounted in rating
- Exploratory data analysis with Pandas, nbviewer, Kaggle Kernel
- Analyzing cardiovascular disease data, nbviewer, Kaggle Kernel
- Decision trees with a toy task and the UCI Adult dataset, nbviewer, Kaggle Kernel
- Linear Regression as an optimization problem, nbviewer, Kaggle Kernel
- Logistic Regression and Random Forest in the credit scoring problem, nbviewer, Kaggle Kernel
- Exploring OLS, Lasso and Random Forest in a regression task, nbviewer, Kaggle Kernel
- Unsupervised learning, nbviewer, Kaggle Kernel
- Implementing online regressor, nbviewer, Kaggle Kernel
- Time series analysis, nbviewer, Kaggle Kernel
- Gradient boosting and flight delays, nbviewer, Kaggle Kernel
Kaggle competitions
- Catch Me If You Can: Intruder Detection through Webpage Session Tracking. Kaggle Inclass
- How good is your Medium article? Kaggle Inclass
Rating
Throughout the course we are maintaining a student rating. It takes into account credits scored in assignments and Kaggle competitions. Top students (according to the final rating) will be listed on a special Wiki page.
Community
Discussions between students are held in the #mlcourse_ai channel of the OpenDataScience Slack team. Fill in this form to get an invitation. The form will also ask you some personal questions, don't hesitate
Wiki Pages
- Prerequisites: Python, math, software, and DevOps β how to get prepared for the course
- 1st session in English: all activities accounted for in rating
The course is free but you can support organizers by making a pledge on Patreon