Python 3 Basics # 6.2 | Implement Matplotlib with Numpy | Python for Beginners

Python 3 Basics # 6.2 | Implement Matplotlib with Numpy | Python for Beginners

Matplotlib with Numpy
a. Scatterplot and it various options to be explored
b. Barplot

Code Starts Here
==============

import numpy as np
# y = m*x + c

m = 3
c = 4

y = m* x + c

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opt = ['-','--','.','.-','|','^','p','D','1','2','s']
color1 = ['r','g','y','b','r','g','y','b','r','g','y']

for i in range(len(opt)):
m = 3
c = 4
y = m* x + c

x = [10,12,14]
y = [6,12,15]

x1 = [11,13,15]
y1 = [8,9,18]

All the playlist of this youtube channel
========================================

1. Data Preprocessing in Machine Learning

2. Confusion Matrix in Machine Learning, ML, AI

3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz

4. Cross Validation, Sampling, train test split in Machine Learning

5. Drop and Delete Operations in Python Pandas

6. Matrices and Vectors with python

7. Detect Outliers in Machine Learning

8. TimeSeries preprocessing in Machine Learning

9. Handling Missing Values in Machine Learning

10. Dummy Encoding Encoding in Machine Learning

11. Data Visualisation with Python, Seaborn, Matplotlib

12. Feature Scaling in Machine Learning

13. Python 3 basics for Beginner

14. Statistics with Python

15. Data Preprocessing in Machine Learning

16. Sklearn Scikit Learn Machine Learning

17. Linear Regression, Supervised Machine Learning

18 Interview Questions on Machine Learning, Artificial Intelligence, Python Pandas and Python Basics

19. Jupyter Notebook Operations