A comprehensive, step-by-step learning repository covering the complete journey from statistics to machine learning model deployment using Python.
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 | 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 |
- ✅ 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
To run the notebooks locally:
git clone https://github.com/udityamerit/Complete-Machine-Learning-For-Beginners.git
cd complete-ml-roadmap
pip install -r requirements.txtThe major libraries used:
numpypandasmatplotlibseabornscikit-learnstatsmodels
All dependencies can be installed via:
pip install -r requirements.txt5.1-Handling_missing_values.ipynb5.2-Handling_Imbalance_dataset.ipynb5.3-Handling_outliers_and_Data_Encoding.ipynb
6.1-EDA_On_Wine_Dataset.ipynb6.2-EDA_On_Flight_Price_Prediction.ipynb6.3-EDA+And+FE+Google+Playstore.ipynb
8.1-Complete_Simple_Linear_Regression.ipynb8.2-Multiple_Linear_Regression.ipynb8.3-Polynomial_Regression.ipynb9.1-Ridge_Lasso_Regression.ipynb
10.1-Basic_Simple_Linear_Regression_Project.ipynb10.2-Multiple_Linear_Regression_Project.ipynb
Uditya Narayan Tiwari 🎓 B.Tech in CSE (AI & ML) @ VIT Bhopal University
This repository is licensed under the MIT License.