Skip to content

update links #866

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Mar 1, 2023
Merged

update links #866

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions section-04-research-and-development/01-machine-learning-pipeline-data-analysis.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4611,8 +4611,8 @@
"source": [
"# Additional Resources\n",
"\n",
"- [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) - Online Course\n",
"- [Packt Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook) - Book\n",
"- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n",
"- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n",
"- [Predict house price with Feature-engine](https://www.kaggle.com/solegalli/predict-house-price-with-feature-engine) - Kaggle kernel\n",
"- [Comprehensive data exploration with Python](https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python) - Kaggle kernel\n",
"- [How I made top 0.3% on a Kaggle competition](https://www.kaggle.com/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition) - Kaggle kernel"
Expand Down
10 changes: 5 additions & 5 deletions section-04-research-and-development/02-machine-learning-pipeline-feature-engineering.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1652,7 +1652,7 @@
"\n",
"For the remaining categorical variables, we will group those categories that are present in less than 1% of the observations. That is, all values of categorical variables that are shared by less than 1% of houses, well be replaced by the string \"Rare\".\n",
"\n",
"To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
"To learn more about how to handle categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
]
},
{
Expand Down Expand Up @@ -1805,7 +1805,7 @@
"\n",
"We will do it so that we capture the monotonic relationship between the label and the target.\n",
"\n",
"To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
"To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
]
},
{
Expand Down Expand Up @@ -3083,9 +3083,9 @@
"\n",
"# Additional Resources\n",
"\n",
"- [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) - Online Course\n",
"- [Packt Feature Engineering Cookbook](https://www.packtpub.com/data/python-feature-engineering-cookbook) - Book\n",
"- [Feature Engineering for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-engineering-for-machine-learning-a-comprehensive-overview-a7ad04c896f8) - Article\n",
"- [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning) - Online Course\n",
"- [Packt Feature Engineering Cookbook](https://www.amazon.com/Python-Feature-Engineering-Cookbook-transforming-dp-1804611301/dp/1804611301) - Book\n",
"- [Feature Engineering for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-engineering-for-machine-learning/) - Article\n",
"- [Practical Code Implementations of Feature Engineering for Machine Learning with Python](https://towardsdatascience.com/practical-code-implementations-of-feature-engineering-for-machine-learning-with-python-f13b953d4bcd) - Article"
]
},
Expand Down
12 changes: 10 additions & 2 deletions section-04-research-and-development/03-machine-learning-pipeline-feature-selection.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -937,9 +937,17 @@
"source": [
"# Additional Resources\n",
"\n",
"- [Feature Selection for Machine Learning](https://www.udemy.com/course/feature-selection-for-machine-learning/?referralCode=186501DF5D93F48C4F71) - Online Course\n",
"- [Feature Selection for Machine Learning: A comprehensive Overview](https://trainindata.medium.com/feature-selection-for-machine-learning-a-comprehensive-overview-bd571db5dd2d) - Article"
"- [Feature Selection for Machine Learning](https://www.trainindata.com/p/feature-selection-for-machine-learning) - Online Course\n",
"- [Feature Selection in Machine Learning with Python](https://leanpub.com/feature-selection-in-machine-learning/) - Book\n",
"- [Feature Selection for Machine Learning: A comprehensive Overview](https://www.blog.trainindata.com/feature-selection-for-machine-learning/) - Article"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand Down
0 ...learning-pPipeline-scoring-new-data.ipynb → ...-learning-pipeline-scoring-new-data.ipynb
View file
Open in desktop
File renamed without changes.
2 changes: 1 addition & 1 deletion section-04-research-and-development/06-feature-engineering-with-open-source.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2019,7 +2019,7 @@
"\n",
"We will do it so that we capture the monotonic relationship between the label and the target.\n",
"\n",
"To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.udemy.com/course/feature-engineering-for-machine-learning/?referralCode=A855148E05283015CF06) in Udemy."
"To learn more about how to encode categorical variables visit our course [Feature Engineering for Machine Learning](https://www.trainindata.com/p/feature-engineering-for-machine-learning)."
]
},
{
Expand Down
4 changes: 2 additions & 2 deletions section-04-research-and-development/08-final-machine-learning-pipeline.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1081,7 +1081,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -1095,7 +1095,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
"version": "3.10.5"
},
"toc": {
"base_numbering": 1,
Expand Down