https://github.com/udityamerit/complete-machine-learning-for-beginners
This repository is structured as a complete ML roadmap combining theory (PDFs) with hands-on coding (Jupyter Notebooks) to help you build a solid foundation in data science and machine learning. Ideal for students, self-learners, and professionals looking to revise or upgrade.
https://github.com/udityamerit/complete-machine-learning-for-beginners
artificial-intelligence classification clustering clustering-algorithm machine machine-learning machinelearning matplotlib matplotlib-figures numpy pandas regression regression-models regressionalgorithms regressionanalysis scikit-learn scikitlearn-machine-learning scipy seaborn tensorflow
Last synced: 2 months ago
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This repository is structured as a complete ML roadmap combining theory (PDFs) with hands-on coding (Jupyter Notebooks) to help you build a solid foundation in data science and machine learning. Ideal for students, self-learners, and professionals looking to revise or upgrade.
- Host: GitHub
- URL: https://github.com/udityamerit/complete-machine-learning-for-beginners
- Owner: udityamerit
- License: mit
- Created: 2025-07-10T19:39:44.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-27T19:33:06.000Z (3 months ago)
- Last Synced: 2025-07-27T20:32:50.483Z (3 months ago)
- Topics: artificial-intelligence, classification, clustering, clustering-algorithm, machine, machine-learning, machinelearning, matplotlib, matplotlib-figures, numpy, pandas, regression, regression-models, regressionalgorithms, regressionanalysis, scikit-learn, scikitlearn-machine-learning, scipy, seaborn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 71.4 MB
- Stars: 3
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
🧠 Complete Machine Learning Roadmap For Beginners
A comprehensive, step-by-step learning repository covering the complete journey from statistics to machine learning model deployment using Python.
---
## 📘 Overview
This repository is structured as a **complete ML roadmap** combining theory (PDFs) with hands-on coding (Jupyter Notebooks) to help you build a solid foundation in data science and machine learning. Ideal for students, self-learners, and professionals looking to revise or upgrade.
---
## 🗂️ Folder Structure
| Folder | Description |
|--------|-------------|
| `0-Dataset` | Contains all datasets used in the course |
| `1-Getting Started With Statistics` | Basics of descriptive statistics and ML relevance |
| `2-Introduction To Probability` | Covers probability rules, addition/multiplication (with PDFs) |
| `3-Probability Distribution Function` | Common distributions: Normal, Binomial, Poisson, etc. |
| `4-Inferential Statistics` | Concepts like hypothesis testing, p-values, confidence intervals |
| `5-Feature Engineering` | Handling missing data, outliers, SMOTE, encoding |
| `6-Exploratory Data Analysis (EDA)` | EDA on Wine, Flights, and Play Store datasets |
| `7-Introduction To Machine Learning` | Basic concepts, types of ML, model workflow |
| `8-Complete Linear Regression` | Simple, Multiple & Polynomial Regression from scratch |
| `9-Ridge, Lasso & ElasticNet` | Regularization techniques for robust modeling |
| `10-Project Implementation` | Mini-projects applying linear models on real data |
---
## 🔍 Key Features
- ✅ Beginner to Intermediate level ML roadmap
- 📚 Theory + Jupyter-based code implementation
- 📊 Real-world datasets used
- 🧠 Covers statistical reasoning behind ML
- 🚀 Final projects for practical application
---
## 💻 Installation
To run the notebooks locally:
```bash
git clone https://github.com/udityamerit/Complete-Machine-Learning-For-Beginners.git
cd complete-ml-roadmap
pip install -r requirements.txt
````
---
## 📦 Dependencies
The major libraries used:
* `numpy`
* `pandas`
* `matplotlib`
* `seaborn`
* `scikit-learn`
* `statsmodels`
All dependencies can be installed via:
```bash
pip install -r requirements.txt
```
---
## 📁 Notable Notebooks
### 📌 Feature Engineering
* `5.1-Handling_missing_values.ipynb`
* `5.2-Handling_Imbalance_dataset.ipynb`
* `5.3-Handling_outliers_and_Data_Encoding.ipynb`
### 📌 Exploratory Data Analysis
* `6.1-EDA_On_Wine_Dataset.ipynb`
* `6.2-EDA_On_Flight_Price_Prediction.ipynb`
* `6.3-EDA+And+FE+Google+Playstore.ipynb`
### 📌 Regression Models
* `8.1-Complete_Simple_Linear_Regression.ipynb`
* `8.2-Multiple_Linear_Regression.ipynb`
* `8.3-Polynomial_Regression.ipynb`
* `9.1-Ridge_Lasso_Regression.ipynb`
### 📌 Mini Projects
* `10.1-Basic_Simple_Linear_Regression_Project.ipynb`
* `10.2-Multiple_Linear_Regression_Project.ipynb`
---
## 👨💻 Author
**Uditya Narayan Tiwari**
🎓 B.Tech in CSE (AI & ML) @ VIT Bhopal University
🔗 [Portfolio Website](https://udityanarayantiwari.netlify.app/)
📂 [GitHub Profile](https://github.com/udityamerit)
💼 [LinkedIn](https://www.linkedin.com/in/uditya-narayan-tiwari-562332289/)
---
## 📄 License
This repository is licensed under the [MIT License](./LICENSE).