https://github.com/j3-signalroom/j3-ml_ai-education_experiences

Some of the project notebooks I built during my deepatlas.ai well-rounded education program.
https://github.com/j3-signalroom/j3-ml_ai-education_experiences

artificial-intelligence artificial-neural-networks deep-learning deep-neural-networks machine-learning

Last synced: 11 days ago
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Some of the project notebooks I built during my deepatlas.ai well-rounded education program.

• Host: GitHub
• URL: https://github.com/j3-signalroom/j3-ml_ai-education_experiences
• Owner: j3-signalroom
• License: mit
• Created: 2024-07-23T00:50:26.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-23T14:43:42.000Z (4 months ago)
• Last Synced: 2024-10-12T23:38:12.171Z (25 days ago)
• Topics: artificial-intelligence, artificial-neural-networks, deep-learning, deep-neural-networks, machine-learning
• Language: Jupyter Notebook
• Homepage: https://linkedin.com/in/jeffreyjonathanjennings
• Size: 1.42 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# 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)