Machine Learning Python Tools.

 Pandas

Data Frame Library

Numpy

Calculation and Matrix Library (Linear Algebra)

Scikit-learn

Machine Learning Library

IPython / Jupyter Notebook

Machine learning program simplification writing tool

IPython / Jupyter Notebook Introduction

There are only two kinds of languages: the ones people complain about and the ones nobody uses - Bjarne Stroustrup

Python Library for machine learning

Python libraries to be used for machine learning:

NumPy - for scientific calculations

pandas - data frame

matplotlib - for plotting two and three-dimensional graphs

scikit-learn

Machine learning algorithm

Data pre-processing

Predictive model building and performance testing

... much more

IPython Notebook / Jupyter Notebook

To create a Painless Machine Learning model

IPython Notebook / Jupyter Notebook

Jupyter Notebook was formerly known as IPython Notebook.

Why you need to know about IPython Notebook?

A notebook is one of the things we use. It would not be wrong to call the IPython Notebook a programmer's notebook.

The IPython Notebook is the perfect tool for machine learning, since machine learning tasks are wearable, meaning that in addition to working, you often have to check the front and back of the job.

In the case of code sharing, we share the code but the person with whom it is shared must see the code run. Documents are shareable in the case of the IPython Notebook. The output of each command or command bundle can be shared through a single document.

Another big advantage is that the IPython Notebook fully supports Markdown formatting. If you wish, you can write the conversation in the form of notes in the Markdown format.

IPython Notebook supports other languages ​​besides Python: C #, Scala, PHP .. etc, but in that case, you have to use the plugin.

Running IPython Notebook

Open cmd and type python notebook then press Enter. If it does not work, write jupyter notebook.

One of the two will work, if not, reinstall the Anaconda package.

Notebook demo

A short demo

Basic instructions

Enter the code and press Enter to write in a new line

Pressing Shift + Enter will execute a cell

Notes and codes together

IPython Notebook!

%matplotlib inline

from matplotlib import pyplot as plt

import numpy as np

x = np.array(range(10))

y = np.array(range(10))

plt.plot(x, y)

plt.show()

picture source:https://ml.howtocode.dev/workflow