-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #4 from j3-signalroom/github_issue-3
Completed Issue #3.
- Loading branch information
Showing
1 changed file
with
16 additions
and
1 deletion.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1 +1,16 @@ | ||
# J3 ML/AI Education Experiences | ||
# J3 ML/AI Education Experiences | ||
During my excellent [Deep Atlas boot camp](https://deepatlas.ai) during the Summer of 2024, I had to complete several project notebooks. Below is a list of some of them: | ||
|
||
## Shallow Learning | ||
Shallow learning, also known as traditional machine learning, is a type of machine learning model with a simple structure that uses one or a few layers of processing units. These layers perform computations on input data, such as applying transformations or combining information from different sources. Examples of shallow learning models include linear regression, decision trees, logistic regression, and support vector machines. | ||
|
||
### [Classification task type project notebook](shallow_learning/j3-shallow_learning_algorithms-classification.ipynb) | ||
Shallow learning, also known as traditional machine learning, is a type of machine learning model with a simple structure that uses one or a few layers of processing units. These layers perform computations on input data, such as applying transformations or combining information from different sources. Examples of shallow learning models include linear regression, decision trees, logistic regression, and support vector machines. | ||
|
||
Project Notebook demostrates the following shallow learning algorithms: | ||
- [Naive Bayes classifiers](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) | ||
- [Linear Support Vector Machine classifier](https://en.wikipedia.org/wiki/Support_vector_machine#:~:text=In%20the%20case%20of%20support,is%20called%20a%20linear%20classifier) | ||
- [XG Boost (eXtreme Gradient Boosting) classifier](https://www.nvidia.com/en-us/glossary/xgboost/) | ||
- [Random Forest classifier](https://www.ibm.com/topics/random-forest) | ||
- [Logistic Regression Model classifier](https://en.wikipedia.org/wiki/Logistic_regression#:~:text=The%20logistic%20regression%20model%20itself,than%20the%20cutoff%20as%20one) | ||
- [Decision Tree classifier](https://www.ibm.com/topics/decision-trees#:~:text=A%20decision%20tree%20is%20a,internal%20nodes%20and%20leaf%20nodes) |