This repository serves as a centralized collection of Python algorithms and resources for Data Science, Machine Learning (ML), and Artificial Intelligence (AI). All code examples and projects were developed and tested using Google Colab notebooks.
This repository emphasizes practical application and foundational knowledge across various stages of the data science and ML pipeline.
| Category | Description |
|---|---|
| Datasets | Datasets to Make the Experiments. |
| ML Tools | Set of tools and frameworks to apply ML. |
| Papers | Set of Relevant Papers of DataScience, ML and AI Areas. |
| Power BI | Example of Power BI Application. |
| Presentations | Set of Relevant Presentations of DataScience, ML and AI Areas. |
| Viewing / Analytics | Codes to generate insightful visualizations - Exploratory Data Analysis (EDA). |
| Pre-processing | Codes for Data cleaning, transformation, normalization, handling missing values, etc. |
| Dimensionality Reduction | Codes for reducing the number of features in a Dataset. |
| Specific Errors | Techniques to identifying and resolving specifics issues in ML datasets and models. |
| Rule-based Techniques | Techniques based on rules instead of statistical learning from data. |
| ML Techniques | ML algorithms, including supervised, unsupervised, and deep learning methods. |
| Colab Basic Concepts | Notebooks covering fundamental usage and features of the Google Colab. |
| Python Basic Concepts | Python syntax, data structures, and concepts for scientific computing. |