This is a repository that I've created to track my progress in AI/Data Science related topics in order to organise and share encountered information and resources. The goal of this repo is to organize/retrieve my ML/AI work, and to share the study material for others in the same boat.
| Course | Where | Timeline | Links |
|---|---|---|---|
| CS383 - Machine Learning | In-Person : Drexel University | Winter 2019-2020 | Link |
| Neural Networks and Deep Learning - Deeplearning.ai | Online-Coursera | March 20, 2020 - April 22, 2020 | Link |
| Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - Deeplearning.ai | Online-Coursera | May 01, 2020 - Present | Link |
In this section I want to share the accumulated information about various Machine learning and deep algorithms, used libraries and more. The Priority here is to show how the algorithm works - not to solve complex and ambitious problems, usually on classical or generated datasets. I'll try to make updates to the readme in chronological order so that any explorer is able to sequentially decipher the implementation.
| Algorithm | Description | Implementation | Dataset |
|---|---|---|---|
| Dimentionality reduction - PCA | Dimentional reduction of 154 images | Python/Matlab | Yalefaces |
| k-Means Clustering | Compared result with Linear Regression | Python/Matlab | Pima-Indians Diabetes Dataset |
| Linear Regression | Predicting the length of fish | Python | Fish-length Dataset |
| Logistic Regression | Gradient descent with sigmoid - predict spam/non-spam | Python | Spabase dataset |
| Naive Bayes | Classifying spam/non-spam | Python | Spabase dataset |
- Watch the coursera videos at 1.25x. I feel it's too slow.
- Aggregated Notes were prepared by using a Rocketbook. I highly recommend getting one.
- E-mail: [email protected]
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