Welcome to my Machine Learning portfolio repository! Here, you'll find implementations and examples of various machine learning algorithms and techniques using Python and popular libraries such as NumPy, pandas, seaborn, scikit-learn, and more.

Introduction to Machine Learning

• Overview of machine learning concepts and applications.

Data Handling

• Utilization of pandas for data manipulation and preprocessing.

Visualization

• Exploratory data analysis and visualization using seaborn.

Supervised Learning

• Implementation of linear regression, logistic regression, support vector machines (SVM), and decision trees.

Unsupervised Learning

• Techniques including t-SNE, PCA (Principal Component Analysis), and clustering algorithms.

Neural Networks

• Multi-layer Perceptron (MLP) for deep learning tasks.

Ensemble Methods

• Random Forest (RF) and boosting techniques.

Anomaly Detection

• Outlier detection using libraries like PyOD and Isolation Forest (iForest).

Projects

• End-to-end machine learning projects demonstrating the integration of various techniques.

• Each directory corresponds to a specific topic or technique.
• Navigate into each directory to find relevant Python scripts, Jupyter notebooks, and README files for detailed explanations and usage instructions.

To get started, clone this repository:

git clone https:https://github.com/YashNawale26/Machine-Learning.git