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Data Science, AI, Machine Learning, and Visualization

This repository serves as a comprehensive knowledge base and use case collection for data science, artificial intelligence, machine learning, and data visualization. It covers multiple technologies, sample exercises, data preprocessing techniques, and applications across a variety of disciplines such as Bioinformatics, Deep Learning, Data Security & Analysis, and Algorithms.

Disciplines Covered:

  • Bioinformatics
  • Deep Learning
  • Data Security & Analysis
  • Algorithm Design
  • Machine Learning
  • Data Visualization
  • AI
  • Data Visualization

Technologies and Methods Featured:

  • R, Python
  • Seaborn, Matplotlib
  • Object-Oriented Design
  • Shiny (R web applications)
  • Outlier detection
  • Data preprocessing
  • Jupyter Notebook
  • Data Visualization
  • Machine Learning
  • Python
  • Data Visualization
  • Machine Learning

Python, R for Data Science, AI/ML Tools:

Here are some essential Python libraries used in data science, AI, and machine learning that you may need:

  • Python 3 Standard Library: Documentation for built-in Python features.
  • NumPy: A library for numerical computations with arrays.
  • Pandas: A powerful tool for data manipulation and analysis.
  • Matplotlib: A popular plotting library for creating static and interactive visualizations.
  • R: Free software environment for statistical computing and graphics.
  • RStudio IDE: Integrated development environment (IDE) for R with powerful tools for plotting, debugging, and workspace management.
  • Jupyter Notebook: An open-source web application for creating and sharing live code, visualizations, and narrative text.
  • Anaconda Navigator: A desktop GUI for managing data science environments and packages for Python and R without using the command line.
  • Install R - Download and install R from the official website.
  • Install RStudio - Download the free version of RStudio Desktop.
    *Install Anaconda Navigator ** - Use Anaconda Navigator to set up Python for data science projects.
  • Jupyter Notebook - Use the free online Jupyter Notebook for testing Python code without installation.

References:

Links: