Welcome to the Machine Learning Repository! This repository is a comprehensive resource for learning the basics of Python, essential libraries like NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn, as well as various aspects of machine learning including data preprocessing, data standardization, and hands-on projects.
- Introduction
- Getting Started
- Python Basics
- Python Libraries for Data Science
- Data Preprocessing
- Data Standardization
- Machine Learning Projects
Python Basics Before diving into data science and machine learning, it's crucial to have a good grasp of Python basics. You can start with the following resources:
Basic Syntax Data Types and Variables Control Flow (if, for, while) Functions and Modules File Handling Refer to the python_basics/ directory for example scripts and notebooks.
Python Libraries for Data Science NumPy NumPy is the fundamental package for scientific computing in Python. It provides support for arrays, matrices, and many mathematical functions.
Installation: pip install numpy Getting Started: NumPy Documentation Check the numpy_tutorial/ directory for practical examples and tutorials.
Pandas Pandas is a powerful data manipulation and analysis library. It provides data structures like Series and DataFrame.
Installation: pip install pandas Getting Started: Pandas Documentation Explore the pandas_tutorial/ directory for detailed examples.
Matplotlib Matplotlib is a plotting library for creating static, animated, and interactive visualizations in Python.
Installation: pip install matplotlib Getting Started: Matplotlib Documentation Find example plots in the matplotlib_tutorial/ directory.
Seaborn Seaborn is a statistical data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Installation: pip install seaborn Getting Started: Seaborn Documentation See the seaborn_tutorial/ directory for various visualization examples.
Scikit-learn Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis.
Installation: pip install scikit-learn Getting Started: Scikit-learn Documentation Refer to the scikit_learn_tutorial/ directory for machine learning examples and projects.
Data Preprocessing Data preprocessing is a crucial step in any machine learning project. It involves cleaning and transforming raw data into a usable format. Topics covered include:
Handling Missing Values Encoding Categorical Data Feature Scaling Examples and detailed explanations are available in the data_preprocessing/ directory.
Data Standardization Standardizing data is essential for many machine learning algorithms. It involves scaling the data so that it has a mean of zero and a standard deviation of one.
Check out the data_standardization/ directory for more information and examples on how to standardize your data.
Machine Learning Projects This repository includes several hands-on projects to apply the concepts learned:
Project 1: Regression Analysis
Project 2: Classification Tasks
Project 3: Clustering Analysis
Project 4: Natural Language Processing
Project 5: Deep Learning with Neural Networks