forked from Yorko/mlcourse.ai
-
Notifications
You must be signed in to change notification settings - Fork 0
/
README.md.orig
77 lines (58 loc) · 12.1 KB
/
README.md.orig
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
## [mlcourse.ai](https://mlcourse.ai), open Machine Learning course
:ru: [Russian version](https://github.com/Yorko/mlcourse.ai/wiki/About-the-course-(in-Russian)) :ru:
:exclamation: The next session launches on **October 1, 2018**. Fill in [this form](https://drive.google.com/open?id=1_pDNuVHwBxV5wuOcdaXoxBZneyAQcqfOl4V2qkqKbNQ) to participate. In September, you'll get an invitation to OpenDataScience Slack team :exclamation:
Mirrors (:uk:-only): [mlcourse.ai](https://mlcourse.ai) (main site), [Kaggle Dataset](https://www.kaggle.com/kashnitsky/mlcourse) (same notebooks as Kernels)
### Outline
This is the list of published articles on medium.com [:uk:](https://medium.com/open-machine-learning-course), habr.com [:ru:](https://habr.com/company/ods/blog/344044/), and jqr.com [:cn:](https://www.jqr.com). Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package.
1. Exploratory Data Analysis with Pandas [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-1-exploratory-data-analysis-with-pandas-de57880f1a68) [:ru:](https://habrahabr.ru/company/ods/blog/322626/) [:cn:](https://www.jqr.com/article/000079), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/topic-1-exploratory-data-analysis-with-pandas)
2. Visual Data Analysis with Python [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-2-visual-data-analysis-in-python-846b989675cd) [:ru:](https://habrahabr.ru/company/ods/blog/323210/) [:cn:](https://www.jqr.com/article/000086), Kaggle Kernels: [part1](https://www.kaggle.com/kashnitsky/topic-2-visual-data-analysis-in-python), [part2](https://www.kaggle.com/kashnitsky/topic-2-part-2-seaborn-and-plotly)
3. Classification, Decision Trees and k Nearest Neighbors [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-3-classification-decision-trees-and-k-nearest-neighbors-8613c6b6d2cd) [:ru:](https://habrahabr.ru/company/ods/blog/322534/) [:cn:](https://www.jqr.com/article/000139), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/topic-3-decision-trees-and-knn)
4. Linear Classification and Regression [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-4-linear-classification-and-regression-44a41b9b5220) [:ru:](https://habrahabr.ru/company/ods/blog/323890/) [:cn:](https://www.jqr.com/article/000175), Kaggle Kernels: [part1](https://www.kaggle.com/kashnitsky/topic-4-linear-models-part-1-ols), [part2](https://www.kaggle.com/kashnitsky/topic-4-linear-models-part-2-classification), [part3](https://www.kaggle.com/kashnitsky/topic-4-linear-models-part-3-regularization), [part4](https://www.kaggle.com/kashnitsky/topic-4-linear-models-part-4-more-of-logit), [part5](https://www.kaggle.com/kashnitsky/topic-4-linear-models-part-5-validation)
5. Bagging and Random Forest [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-5-ensembles-of-algorithms-and-random-forest-8e05246cbba7) [:ru:](https://habrahabr.ru/company/ods/blog/324402/) [:cn:](https://www.jqr.com/article/000241), Kaggle Kernels: [part1](https://www.kaggle.com/kashnitsky/topic-5-ensembles-part-1-bagging), [part2](https://www.kaggle.com/kashnitsky/topic-5-ensembles-part-2-random-forest), [part3](https://www.kaggle.com/kashnitsky/topic-5-ensembles-part-3-feature-importance)
6. Feature Engineering and Feature Selection [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-6-feature-engineering-and-feature-selection-8b94f870706a) [:ru:](https://habrahabr.ru/company/ods/blog/325422/) [:cn:](https://www.jqr.com/article/000249), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/topic-6-feature-engineering-and-feature-selection)
7. Unsupervised Learning: Principal Component Analysis and Clustering [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-7-unsupervised-learning-pca-and-clustering-db7879568417) [:ru:](https://habrahabr.ru/company/ods/blog/325654/) [:cn:](https://www.jqr.com/article/000336), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/topic-7-unsupervised-learning-pca-and-clustering)
8. Vowpal Wabbit: Learning with Gigabytes of Data [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-8-vowpal-wabbit-fast-learning-with-gigabytes-of-data-60f750086237) [:ru:](https://habrahabr.ru/company/ods/blog/326418/) [:cn:](https://www.jqr.com/article/000348), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/topic-8-online-learning-and-vowpal-wabbit)
9. Time Series Analysis with Python, part 1 [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-9-time-series-analysis-in-python-a270cb05e0b3) [:ru:](https://habrahabr.ru/company/ods/blog/327242/) [:cn:](https://www.jqr.com/article/000450). Predicting future with Facebook Prophet, part 2 [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-9-part-3-predicting-the-future-with-facebook-prophet-3f3af145cdc), Kaggle Kernels: [part1](https://www.kaggle.com/kashnitsky/topic-9-part-1-time-series-analysis-in-python), [part2](https://www.kaggle.com/kashnitsky/topic-9-part-2-time-series-with-facebook-prophet)
10. Gradient Boosting [:uk:](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-10-gradient-boosting-c751538131ac) [:ru:](https://habrahabr.ru/company/ods/blog/327250/), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/topic-10-gradient-boosting)
### Lectures
Videolectures are uploaded to [this](https://bit.ly/2zY6Xe2) YouTube playlist.
Introduction, [video](https://youtu.be/QKTuw4PNOsU), [slides](https://bit.ly/2NuadRV)
<<<<<<< HEAD
1. Exploratory data analysis with Pandas, [video](https://youtu.be/fwWCw_cE5aI). Discussion of the 1st demo assignment is [here](https://youtu.be/Twpn7ihVWjQ)
=======
1. Exploratory data analysis with Pandas, [video](https://youtu.be/fwWCw_cE5aI)
2. Visualization, main plots for EDA, [video](https://www.youtube.com/watch?v=WNoQTNOME5g)
3. Decision trees: [theory](https://youtu.be/H4XlBTPv5rQ) and [practical part](https://youtu.be/RrVYO6Td9Js)
4. Logistic regression: [theoretical foundations](https://www.youtube.com/watch?v=l3jiw-N544s), [practical part](https://www.youtube.com/watch?v=7o0SWgY89i8) (baselines in the "Alice" competition)
### Assignments
1. Exploratory Data Analysis of Olympic games with Pandas, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_fall2018/assignment1_pandas_olympic.ipynb?flush_cache=true). Deadline: **October 14, 21:59 UTC+2**
2. Exploratory Data Analysis of US flights, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_fall2018/assignment2_USA_flights.ipynb?flush_cache=true). Deadline: **October 21, 21:59 UTC+2**
3. Decision trees. [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_fall2018/assignment3_decision_trees.ipynb?flush_cache=true). Deadline: **October 28, 21:59 UTC+2**. Optional: implementing a decision tree algorithm, [nvbiewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_fall2018/assignment3_optional_implement_dt.ipynb?flush_cache=true) (no webforms and credits, the same deadline)
4. Logisitic regression. [nbviewer](notebooks/blob/master/jupyter_english/assignments_fall2018/assignment4_websites_logistic_regression.ipynb). Deadline: **November 4, 21:59 UTC+2**
5. Random Forest and Logistic Regression in credit scoring and movie reviews classification. [nbviewer](notebooks/blob/master/jupyter_english/assignments_fall2018/assignment5_rf_logit_scoring_texts.ipynb). Deadline: **November 11, 21:59 UTC+2**
>>>>>>> upstream/master
### Assignments
1. Exploratory Data Analysis of Olympic games with Pandas, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_fall2018/assignment1_pandas_olympic.ipynb?flush_cache=true). Deadline: **October 14, 20:59 CET**
2. Exploratory Data Analysis of US flights, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_fall2018/assignment2_USA_flights.ipynb?flush_cache=true). Deadline: **October 21, 20:59 CET**
Demo assignments, just for practice, not to be accounted in rating
1. Exploratory data analysis with Pandas, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment01_pandas_uci_adult.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-1-pandas-and-uci-adult-dataset)
2. Analyzing cardiovascular disease data, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment02_analyzing_cardiovascular_desease_data.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-2-analyzing-cardiovascular-data)
3. Decision trees with a toy task and the UCI Adult dataset, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment03_decision_trees.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-3-decision-trees)
4. Linear Regression as an optimization problem, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment04_linreg_optimization.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-4-linear-regression-as-optimization)
5. Logistic Regression and Random Forest in the credit scoring problem, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment05_logit_rf_credit_scoring.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-5-logit-and-rf-for-credit-scoring)
6. Exploring OLS, Lasso and Random Forest in a regression task, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment06_regression_wine.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-6-linear-models-and-rf-for-regression)
7. Unsupervised learning, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment07_unsupervised_learning.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-7-unupervised-learning)
8. Implementing online regressor, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment08_implement_sgd_regressor.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-8-implementing-online-regressor)
9. Time series analysis, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment09_time_series.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-9-time-series-analysis)
10. Gradient boosting and flight delays, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment10_flight_delays_kaggle.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-10-gradient-boosting-and-flight-delays)
### Kaggle competitions
1. Catch Me If You Can: Intruder Detection through Webpage Session Tracking. [Kaggle Inclass](https://www.kaggle.com/c/catch-me-if-you-can-intruder-detection-through-webpage-session-tracking2)
2. How good is your Medium article? [Kaggle Inclass](https://www.kaggle.com/c/how-good-is-your-medium-article/)
### 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](https://drive.google.com/open?id=1_pDNuVHwBxV5wuOcdaXoxBZneyAQcqfOl4V2qkqKbNQ) to get an invitation. The form will also ask you some personal questions, don't hesitate :wave:
### More info
Go to [mlcourse.ai](https://mlcourse.ai)
*The course is free but you can support organizers by making a pledge on [Patreon](https://www.patreon.com/ods_mlcourse)*