2. Data Preprocessing - Part 1 | Data Preprocessing in Machine learning
Introduction to Data Preprocessing in Machine Learning
Overview of the Video
- This video focuses on writing Python code for data preprocessing using Google Colab, a cloud-based coding environment.
- The presenter emphasizes that while Google Colab is preferred, other platforms like Jupyter Notebook can also be used.
Getting Started with Google Colab
- Google Colab is described as an easy-to-access, free notebook environment that supports popular machine learning libraries without requiring setup.
- To access Google Colab, users should search for "Google Collaboratory" and navigate to collab.research.google.com.
- Users can create a new notebook or select from previous ones; the presenter demonstrates creating a new notebook.
Navigating the Google Colab Interface
Understanding the Environment
- The interface allows users to add code cells and text cells easily; renaming files is also straightforward through the file menu.
- Files are saved as
.ipynbformat and can be stored in Google Drive or downloaded in different formats such as.py.
Importing Libraries for Machine Learning
Essential Libraries
- The first step in any machine learning project is importing necessary libraries: NumPy, Matplotlib, and Pandas are highlighted as essential tools.
- NumPy: Used for handling arrays which are crucial for machine learning models' input requirements. It’s imported with a shortcut
np.
- Matplotlib: Specifically its
pyplotmodule (imported asplt), is utilized for plotting charts and visualizations. This library enhances data representation capabilities.
- Pandas: A powerful library used not only for importing datasets but also for managing features and dependent variables efficiently; it’s imported with a shortcut
pd.
Importing Process
- Each library import follows a standard format starting with the keyword
import, followed by the library name and an optional alias usingas. This practice streamlines code readability and efficiency when calling functions later on.
- After setting up imports, users can run their code cells either by clicking play buttons or through runtime options available in the menu bar. Successful execution confirms proper setup of libraries without errors.
Next Steps in Data Preprocessing
Moving Forward
- In upcoming videos, viewers will learn how to upload datasets into Google Colab after successfully importing libraries.
- Viewers are encouraged to implement their first steps by practicing library imports before proceeding further into dataset management techniques within Google Colab.