Machine Learning Repository

Welcome to the Machine Learning Repository — a well-structured collection of resources that covers essential topics in Machine Learning, Data Science, and related foundational concepts. This repository is designed to help learners and practitioners build a strong understanding of machine learning from the ground up.

📁 Repository Structure

This repository is organized into the following folders:

1️⃣ Python Basics
Covers intermediate Python concepts essential for ML development:

• Flask & Streamlit (Web Applications)
• Logging in Python
• Memory Management
• Multi-threading & Multi-processing

2️⃣ SQL and SQLite
Fundamental SQL concepts for data retrieval, manipulation, and management:

• Hands-on SQL queries
• SQLite database operations

3️⃣ Data
A collection of datasets used for Exploratory Data Analysis (EDA) and Machine Learning projects.

4️⃣ Maths for Machine Learning
Covers three core mathematical concepts essential for ML:

• Linear Algebra: Vectors, Matrices, Eigenvalues, etc.
• Calculus: Derivatives, Gradients, Optimization techniques
• Probability & Statistics: Distributions, Bayes' Theorem, Hypothesis Testing

5️⃣ EDA (Exploratory Data Analysis)
EDA Part 1: Theory
Covers key data preprocessing techniques:

• Handling Missing Values
• Feature Scaling
• Feature Binning
• Feature Encoding
• Outlier Treatment

EDA Part 2: Practical Implementation
Hands-on dataset exploration and visualization.

6️⃣ Feature Engineering
Techniques to improve model performance by transforming raw data into informative features.

7️⃣ Supervised Learning
Covers key machine learning algorithms:

• Regression: Linear Regression, Logistic Regression, KNN Regressor, SVM Regressor, Decision Tree Regressor, Naive Bayes Regressor
• Classification: Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Trees
• Ensemble Methods: Random Forest, AdaBoost, XGBoost, Gradient Boosting

8️⃣ Unsupervised Learning
Covers dimensionality reduction & clustering techniques:

• PCA (Principal Component Analysis)
• Clustering Algorithms: K-Means, DBScan, Hierarchical Clustering
• Anomaly Detection

9️⃣ Docker Basics
Covers fundamental Docker concepts for ML model deployment.

📌 Next Steps: Project Implementations

For well-structured project implementations, visit:
👉 Machine Learning Projects Repository

🚀 Get Started

bash git clone https://github.com/madhulatha777/Machine-Learning.git cd Machine-Learning

🤝 Contributions

Contributions are welcome! Feel free to submit issues, suggestions, or pull requests to enhance this repository.

🛠 Maintained by: Madhulatha Seerapu

📩 Contact: GitHub Profile