Machine Learning A-Z™: Hands-On Python & R In Data
  • Introduction
  • Introduction
    • Introduction
  • Section 1: Welcome to the course!
    • 1. Applications of Machine Learning
    • 2. Why Machine Learning is the Future
    • 3. Important notes, tips & tricks for this course
    • 4. Installing Python and Anaconda (Mac, Linux & Windows)
    • 5. Update: Recommended Anaconda Version
    • 6. Installing R and R Studio (Mac, Linux & Windows)
    • 7. BONUS: Meet your instructors
  • Section 2: Part 1 Data Preprocessing
    • 8. Welcome to Part 1 - Data Preprocessing
    • 9. Get the dataset
    • 10. Importing the Libraries
    • 11. Importing the Dataset
    • 12. For Python learners, summary of Object-oriented programming: classes & objects
    • 13. Missing Data
    • 14. Categorical Data
    • 15. WARNING - Update
    • 16. Splitting the Dataset into the Training set and Test set
    • 17. Feature Scaling
    • 18. And here is our Data Preprocessing Template!
    • Quiz 1: Data Preprocessing
  • Section 3: Part 2 Regression
    • 19. Welcome to Part 2 - Regression
  • Section 4: Simple Linear Regression
    • 20. How to get the dataset
    • 21. Dataset + Business Problem Description
    • 22. Simple Linear Regression Intuition - Step 1
    • 23. Simple Linear Regression Intuition - Step 2
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  • PYTHON IMPORTING THE LIBRARIES
  • Learning how to import the Libraries
  • Activity
  1. Section 2: Part 1 Data Preprocessing

10. Importing the Libraries

PYTHON IMPORTING THE LIBRARIES

Learning how to import the Libraries

  • So we are going to teach you how to import the libraries and the tutorial is going to be split into two parts

  • The first part of the tutorial will be in Python and the second part will be in R.

  • Open up Spyder IDE, in Anaconda Navigator.

Activity

  • So create a Python file named Data_Processing, and we learn how to import Libraries

      # Data Processing

  # Reporting the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd

  • Numpy is the library that we need to include any types of mathematics in our age.

  • Machine learning models are based on mathematics then we will absolutely need Numpy

  • Matplotlib is a library that is going to help us plot nice charts

  • Pandas is really the best library to import data sets and manage data sets.

  • So with that we have imported the three most essential libraries in Python

  • Data_processing.py contains my python code for importing the 3 libraries.

  • If you open RStudio, you do not need to import any libraries because most of the libraries are included in RStudio IDE.

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Last updated 6 years ago