The document highlights five essential Python libraries for machine learning: Pandas for data manipulation, NumPy for scientific computing, Matplotlib for plotting, Scikit-learn for data mining and analysis, and TensorFlow for building and training deep learning models. Each library serves a specific purpose, providing tools for data analysis, array manipulation, visualization, machine learning algorithms, and tensor-based computation. Together, these libraries form a robust toolkit for developers in the machine learning field.

1. 5 Python libraries should know for
Machine learning
import pandas as pd
pandas is a Python package providing fast, flexible, and
expressive data structures designed to make working with
“relational” or “labeled” data both easy and intuitive. It aims to
be the fundamental high-level building block for doing
practical, real world data analysis in Python.

2. 5 Python libraries should know for
Machine learning
import numpy as np
NumPy is the fundamental package for scientific computing
with Python. It contains among other things:
● a powerful N-dimensional array object
● sophisticated (broadcasting) functions
● tools for integrating C/C++ and Fortran code
● useful linear algebra, Fourier transform, and random number
capabilities

3. 5 Python libraries should know for
Machine learning
import mathplotlib
Matplotlib is a Python 2D plotting library which produces
publication quality figures in a variety of hardcopy formats and
interactive environments across platforms. Matplotlib can be
used in Python scripts, the Python and IPython shell, the
jupyter notebook, web application servers, and four graphical
user interface toolkits.
For simple plotting the pyplot module provides a MATLAB-like
interface, particularly when combined with IPython

4. 5 Python libraries should know for
Machine learning
from sklearn.datasets import load_iris
from sklearn.svm import SVC
Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib.
Classification
Regression
Clustering
Dimensionality reduction
Model selection
Preprocessing

5. 5 Python libraries should know for
Machine learning
Import tensorflow as tf
TensorFlow is an open source software library released in
2015 by Google to make it easier for developers to design,
build, and train deep learning models.
At a high level, TensorFlow is a Python library that allows
users to express arbitrary computation as a graph of data
flows. Nodes in this graph represent mathematical operations,
whereas edges represent data that is communicated from
one node to another. Data in TensorFlow are represented as
tensors, which are multidimensional arrays

6. 5 Python libraries should know for
Machine learning
Import tensorflow as tf
TensorFlow is an open source software library released in
2015 by Google to make it easier for developers to design,
build, and train deep learning models.
At a high level, TensorFlow is a Python library that allows
users to express arbitrary computation as a graph of data
flows. Nodes in this graph represent mathematical operations,
whereas edges represent data that is communicated from
one node to another. Data in TensorFlow are represented as
tensors, which are multidimensional arrays