Welcome to the ALL-ABOUT-MACHINE-LEARNING repository! This repository is a comprehensive collection of resources, code, and concepts related to machine learning, data handling, and visualization.
Here is an overview of the folders and their purpose:
- 00_MATPLOTLIB: Learn how to use Matplotlib for creating static, animated, and interactive visualizations.
- 00_NUMPY: Resources and examples for numerical computations with NumPy.
- 00_PANDAS: Tutorials and use cases for data manipulation with Pandas.
- 00_PLOTLY: Interactive visualizations with Plotly.
- 00_SEABORN: Advanced statistical data visualization using Seaborn.
- 01_HANDLING_DATASET: Best practices and code examples for handling datasets.
- 02_KNOWING_ABOUT_THE_DATA: Tools and techniques for understanding the structure and content of datasets.
- 03_UNIVARIATE_ANALYSIS: Performing univariate analysis on datasets.
- 04_BIVARIATE_ANALYSIS: Techniques for analyzing relationships between two variables.
- 05_PANDAS_PROFILING: Automate data exploration and profiling using Pandas Profiling.
- 06_STANDARDIZATION: Standardizing datasets for machine learning workflows.
- 07_NORMALIZATION: Normalizing datasets for better model performance.
- 08_ORDINAL_ENCODING: Encoding ordinal categorical variables.
- 09_ONE_HOT_ENCODING: Encoding nominal categorical variables using one-hot encoding.
- 10_COLUMN_TRANSFORMER: Applying transformations to specific columns in datasets.
- 11_SKLEARN_PIPELINE: Building machine learning pipelines with scikit-learn.
- 12_FUNCTION_TRANSFORMER: Custom transformations using FunctionTransformer in scikit-learn.
- 13_POWER_TRANSFORMER: Power transformations for stabilizing variance and minimizing skewness.
- 14_BINNING_AND_BINARIZATION: Techniques for binning continuous data and binarization.
- 00_PROLOG: Resources related to Prolog programming language.
- ML_CONCEPTS: Core concepts and algorithms in machine learning.