This repository contains my personal notes and Jupyter notebooks for the machine learning course provided by educative.io. It serves as both a revision toolkit for school modules and a reference guide for machine learning concepts implemented using scikit-learn.
Before running the notebook, ensure that you have the following installed:
- Python 3.x
- Jupyter Notebook or JupyterLab
- scikit-learn
You can install the necessary libraries using pip:
pip install notebook scikit-learn matplotlib seaborn pandas numpy
The project is structured into different folders, each representing a module of the course:
Working with Datasets
Loading and visualizing built-in datasets
Generating synthetic datasets for practice
Preprocessing data for machine learning tasks
Feature Engineering
Techniques for feature selection
Methods for feature extraction
Handling missing values in datasets
LinearRegression.ipynb
The key machine learning concepts covered in this repository include:
- Working with datasets (loading, visualizing, and preprocessing)
- Feature engineering (feature selection, extraction, and handling missing values)
- Linear regression and its implementation using scikit-learn
To view and interact with the notebooks, navigate to the respective folder and open the notebook file (.ipynb) using Jupyter Notebook or JupyterLab. The markdown files (.md) contain concise notes on the topic covered.
- Course Link: Hands-on Machine Learning with scikit-learn
- scikit-learn Documentation: scikit-learn.org
- Add more notebooks and notes for other modules
- Include more examples and exercises for each concept
BTW : I've been postponing the completion of this project as its primary purpose was to serve as a revision aid for a particular school module, which diminished my motivation to finalize it post-course.