🧠 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.

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📘 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.

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🗂️ 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

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🔍 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

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💻 Installation

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.txt

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📦 Dependencies

The major libraries used:

• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn
• statsmodels

All dependencies can be installed via:

pip install -r requirements.txt

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📁 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

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👨‍💻 Author

Uditya Narayan Tiwari 🎓 B.Tech in CSE (AI & ML) @ VIT Bhopal University

🔗 Portfolio Website

📂 GitHub Profile

💼 LinkedIn

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📄 License

This repository is licensed under the MIT License.