50-Days-of-Machine-Learning

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50-Days-of-Machine-Learning

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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.

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#  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!

---

![Animation](https://media.giphy.com/media/JIX9t2j0ZTN9S/giphy.gif)

**Together, let's continue to innovate and explore the endless possibilities of machine learning!** 🚀✨

---

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