https://github.com/adilshamim8/50-days-of-machine-learning
50 Days of Machine Learning is an immersive project designed to guide learners through essential machine learning concepts and techniques, featuring daily hands-on exercises and real-world datasets.
https://github.com/adilshamim8/50-days-of-machine-learning
ai deep-learning machine-learning machine-learning-algorithms
Last synced: 2 months ago
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50 Days of Machine Learning is an immersive project designed to guide learners through essential machine learning concepts and techniques, featuring daily hands-on exercises and real-world datasets.
- Host: GitHub
- URL: https://github.com/adilshamim8/50-days-of-machine-learning
- Owner: AdilShamim8
- Created: 2024-10-31T06:01:33.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-14T15:25:05.000Z (2 months ago)
- Last Synced: 2025-03-14T16:31:51.003Z (2 months ago)
- Topics: ai, deep-learning, machine-learning, machine-learning-algorithms
- Language: Jupyter Notebook
- Homepage:
- Size: 17 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 50 Days of Machine Learning
Welcome to the **50 Days of Machine Learning** project! This repository is designed to take you on a structured journey through essential concepts and techniques in machine learning over the course of 50 days. Each day focuses on a specific topic, allowing you to build your skills progressively.
## 📚 Table of Contents
- [Introduction](#introduction)
- [What's Included](#whats-included)
- [Daily Topics](#daily-topics)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [How to Contribute](#how-to-contribute)
- [Acknowledgments](#acknowledgments)
## 🔍 Introduction
Machine learning is a field that's continually evolving and expanding, offering numerous applications in various industries. This project aims to demystify key concepts, tools, and techniques while providing practical exercises to solidify your understanding.
## 📦 What's Included
- Structured daily topics covering fundamental and advanced machine learning concepts
- Hands-on exercises with Python, utilizing libraries such as Pandas, Scikit-learn, TensorFlow, and Keras
- Real-world datasets to practice data manipulation, analysis, and predictive modeling
---
## 📅 Daily Topics
### Here’s a breakdown of what you’ll learn over the 50 days:
| Day | Topic |
|------|-------------------------------------------------------------|
| 🗓️ Day 00 | **API To DataFrame** |
| 🗓️ Day 1 | **Working with CSV files** |
| 🗓️ Day 2 | **Working with JSON and SQL** |
| 🗓️ Day 3 | **Pandas DataFrame using Web Scraping** |
| 🗓️ Day 4 | **Understanding Your Data - Descriptive Stats** |
| 🗓️ Day 5 | **Univariate Analysis** |
| 🗓️ Day 6 | **Bivariate Analysis** |
| 🗓️ Day 7 | **Pandas Profiling** |
| 🗓️ Day 8 | **Standardization** |
| 🗓️ Day 9 | **Normalization** |
| 🗓️ Day 10 | **Ordinal Encoding** |
| 🗓️ Day 11 | **One-Hot Encoding** |
| 🗓️ Day 12 | **Column Transformer** |
| 🗓️ Day 13 | **Scikit-learn Pipelines** |
| 🗓️ Day 14 | **Function Transformer** |
| 🗓️ Day 15 | **Power Transformer** |
| 🗓️ Day 16 | **Binning and Binarization** |
| 🗓️ Day 17 | **Handling Mixed Variables** |
| 🗓️ Day 18 | **Handling Date and Time** |
| 🗓️ Day 19 | **Complete Case Analysis** |
| 🗓️ Day 20 | **Imputing Numerical Data** |
| 🗓️ Day 21 | **Handling Missing Categorical Data** |
| 🗓️ Day 22 | **Missing Indicator** |
| 🗓️ Day 23 | **KNN Imputer** |
| 🗓️ Day 24 | **Iterative Imputer** |
| 🗓️ Day 25 | **Outlier Removal using Z-Score** |
| 🗓️ Day 26 | **Outlier Removal using IQR Method** |
| 🗓️ Day 27 | **Outlier Detection using Percentiles** |
| 🗓️ Day 28 | **Feature Construction and Feature Splitting** |
| 🗓️ Day 29 | **PCA (Principal Component Analysis)** |
| 🗓️ Day 30 | **Simple Linear Regression** |
| 🗓️ Day 31 | **Regression Metrics** |
| 🗓️ Day 32 | **Multiple Linear Regression** |
| 🗓️ Day 33 | **Gradient Descent** |
| 🗓️ Day 34 | **Types of Gradient Descent** |
| 🗓️ Day 35 | **Polynomial Regression** |
| 🗓️ Day 36 | **Regularized Linear Models** |
| 🗓️ Day 37 | **Lasso Regression** |
| 🗓️ Day 38 | **ElasticNet Regression** |
| 🗓️ Day 39 | **Logistic Regression** |
| 🗓️ Day 40 | **Classification Metrics** |
| 🗓️ Day 41 | **Logistic Regression (continued)** |
| 🗓️ Day 42 | **Random Forest** |
| 🗓️ Day 43 | **AdaBoost** |
| 🗓️ Day 44 | **Stacking and Blending** |
| 🗓️ Day 45 | **Gradient Boosting** |
| 🗓️ Day 46 | **K-Means** |
| 🗓️ Day 47 | **Hierarchical & DBSCAN Clustering** |
---
## 🚀 Getting Started
To start learning, clone the repository to your local machine:
```bash
git clone https://github.com/AdilShamim8/50-Days-of-Machine-Learning.git
cd 50-Days-of-Machine-Learning
```
Each day's folder contains a Jupyter notebook or Python script along with exercises to reinforce the topics covered.
## 🖥️ Prerequisites
To follow along, you should have the following installed:
- Python 3.6 or later
- Jupyter Notebook or another IDE
- Required libraries (install via requirements.txt):
```bash
pip install -r requirements.txt
```
## 🤝 How to Contribute
Contributions are welcome! If you'd like to improve this repository or add resources, please fork the repository, make your changes, and submit a pull request. Please ensure to maintain code style and include comments where necessary.
## Acknowledgments
Special thanks to the machine learning community and various online resources that inspired this project. Your contributions to the field of machine learning make this learning journey possible!
---
**Together, let's continue to innovate and explore the endless possibilities of machine learning!** 🚀✨
---