{"id":29631906,"url":"https://github.com/udityamerit/complete-machine-learning-for-beginners","last_synced_at":"2026-05-15T21:32:54.638Z","repository":{"id":304472395,"uuid":"1017599995","full_name":"udityamerit/Complete-Machine-Learning-For-Beginners","owner":"udityamerit","description":"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. 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003e🧠 Complete Machine Learning Roadmap For Beginners \u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  A comprehensive, step-by-step learning repository covering the complete journey from statistics to machine learning model deployment using Python.\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/udityamerit\"\u003e\u003cimg src=\"https://img.shields.io/github/followers/udityamerit?label=GitHub\u0026style=social\" /\u003e\u003c/a\u003e\n  \u003ca href=\"https://www.linkedin.com/in/uditya-narayan-tiwari-562332289/\"\u003e\u003cimg src=\"https://img.shields.io/badge/LinkedIn-blue?logo=linkedin\u0026style=flat\u0026logoColor=white\" /\u003e\u003c/a\u003e\n  \u003ca href=\"https://udityanarayantiwari.netlify.app/\"\u003e\u003cimg src=\"https://img.shields.io/badge/Portfolio-Visit-green?style=flat\u0026logo=firefox-browser\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n---\n\n## 📘 Overview\n\nThis 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.\n\n---\n\n## 🗂️ Folder Structure\n\n| Folder | Description |\n|--------|-------------|\n| `0-Dataset` | Contains all datasets used in the course |\n| `1-Getting Started With Statistics` | Basics of descriptive statistics and ML relevance |\n| `2-Introduction To Probability` | Covers probability rules, addition/multiplication (with PDFs) |\n| `3-Probability Distribution Function` | Common distributions: Normal, Binomial, Poisson, etc. |\n| `4-Inferential Statistics` | Concepts like hypothesis testing, p-values, confidence intervals |\n| `5-Feature Engineering` | Handling missing data, outliers, SMOTE, encoding |\n| `6-Exploratory Data Analysis (EDA)` | EDA on Wine, Flights, and Play Store datasets |\n| `7-Introduction To Machine Learning` | Basic concepts, types of ML, model workflow |\n| `8-Complete Linear Regression` | Simple, Multiple \u0026 Polynomial Regression from scratch |\n| `9-Ridge, Lasso \u0026 ElasticNet` | Regularization techniques for robust modeling |\n| `10-Project Implementation` | Mini-projects applying linear models on real data |\n\n---\n\n## 🔍 Key Features\n\n- ✅ Beginner to Intermediate level ML roadmap\n- 📚 Theory + Jupyter-based code implementation\n- 📊 Real-world datasets used\n- 🧠 Covers statistical reasoning behind ML\n- 🚀 Final projects for practical application\n\n---\n\n## 💻 Installation\n\nTo run the notebooks locally:\n\n```bash\ngit clone https://github.com/udityamerit/Complete-Machine-Learning-For-Beginners.git\ncd complete-ml-roadmap\npip install -r requirements.txt\n````\n\n---\n\n## 📦 Dependencies\n\nThe major libraries used:\n\n* `numpy`\n* `pandas`\n* `matplotlib`\n* `seaborn`\n* `scikit-learn`\n* `statsmodels`\n\nAll dependencies can be installed via:\n\n```bash\npip install -r requirements.txt\n```\n\n---\n\n## 📁 Notable Notebooks\n\n### 📌 Feature Engineering\n\n* `5.1-Handling_missing_values.ipynb`\n* `5.2-Handling_Imbalance_dataset.ipynb`\n* `5.3-Handling_outliers_and_Data_Encoding.ipynb`\n\n### 📌 Exploratory Data Analysis\n\n* `6.1-EDA_On_Wine_Dataset.ipynb`\n* `6.2-EDA_On_Flight_Price_Prediction.ipynb`\n* `6.3-EDA+And+FE+Google+Playstore.ipynb`\n\n### 📌 Regression Models\n\n* `8.1-Complete_Simple_Linear_Regression.ipynb`\n* `8.2-Multiple_Linear_Regression.ipynb`\n* `8.3-Polynomial_Regression.ipynb`\n* `9.1-Ridge_Lasso_Regression.ipynb`\n\n### 📌 Mini Projects\n\n* `10.1-Basic_Simple_Linear_Regression_Project.ipynb`\n* `10.2-Multiple_Linear_Regression_Project.ipynb`\n\n---\n\n## 👨‍💻 Author\n\n**Uditya Narayan Tiwari**\n🎓 B.Tech in CSE (AI \u0026 ML) @ VIT Bhopal University\n\n🔗 [Portfolio Website](https://udityanarayantiwari.netlify.app/)\n\n📂 [GitHub Profile](https://github.com/udityamerit)\n\n💼 [LinkedIn](https://www.linkedin.com/in/uditya-narayan-tiwari-562332289/)\n\n---\n\n## 📄 License\n\nThis repository is licensed under the [MIT License](./LICENSE).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fudityamerit%2Fcomplete-machine-learning-for-beginners","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fudityamerit%2Fcomplete-machine-learning-for-beginners","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fudityamerit%2Fcomplete-machine-learning-for-beginners/lists"}