This document discusses machine learning with Python. It provides an overview of Python, highlighting that it is easy to learn, has a vast community and documentation, and is versatile. It then defines machine learning and discusses popular Python libraries for machine learning like NumPy, SciPy, Matplotlib, Pandas, and OpenCV. It provides examples of operations that can be performed with OpenCV, like reading and manipulating images. Overall the document serves as an introduction to machine learning with Python and the main libraries used.

1. MACHINE
LEARNING
WITH
PYTHON
VISHAL BISHT
CSE 4th year

5. About Python
• It was created by Guido van Roussum.
• Python is an interpreted high level ,
general purpose programming
language.
• Python was conceived in the late 1980s
as a successor to the ABC language. It
was first released in 1991.

6. Why Python?

8. 1.EASY TO LEARN
Beginner friendly language .You don’t need to
be a hardcore programmer. This independent
language can also be called as one of the most
flexible languages across different platforms
and technologies.
2.VAST COMMUNITY
The constant upgrade by the developer
community support makes Python one of the
most suitable languages for machine learning
applications

9. 3.DOCUMENTATION
This language has extensive tutorials and
documentation. Readability is a primary focus for Python
developers, in both project and code documentation.
4.VERSATILITY
Versatile language supports object-oriented
programming, structured programming, and functional
programming patterns, etc. and can be applied not only
in projects like machine learning.
5.FRAMEWORK AND LIBRARIES
The language has a great number of machine learning
libraries and some of the prominent libraries are such as
TensorFlow, Pytorch, Matplotlib, SciKit Learn, etc.

11. • Machine learning is an application of artificial
intelligence (AI) that provides systems the
ability to automatically learn and improve
from experience without being explicitly
programmed. Machine learning focuses on
the development of computer programs that
can access data and use it learn for
themselves.

13. 1.NumPy
NumPy is a very popular python library for
large multi-dimensional array and matrix
processing, with the help of a large collection
of high-level mathematical functions. It is
particularly useful for linear algebra, Fourier
transform, and random number capabilities.
High-end libraries like TensorFlow uses NumPy
internally for manipulation of Tensors.

14. 2.SciPy
SciPy is a very popular library among Machine
Learning enthusiasts as it contains different
modules for optimization, linear algebra,
integration and statistics. SciPy is also very
useful for image manipulation. It is built on
top of two basic Python libraries, viz., NumPy
and SciPy
Original Image Tinted image Resized tinted
image

15. 3.Matplotlib
• It is a 2D plotting library used for creating 2D
graphs and plots. A module named pyplot
makes it easy for programmers for plotting as
it provides features to control line styles, font
properties, formatting axes, etc. It provides
various kinds of graphs and plots for data
visualization, viz., histogram, error charts, bar
chats, etc,.

16. 4.Pandas
Pandas is a popular Python library for data
analysis. As we know that the dataset must be
prepared before training. It provides high-level
data structures and wide variety tools for data
analysis. It provides many inbuilt methods for
groping, combining and filtering data.
for eg.

17. 5.OpenCV: Operations that we can perform with
openCV library
a. Reading an image : By using imread() fuction.
b. Extracting the RGB values of a pixel.
c. Extracting the Region of Interest (ROI):By slicing
the pixel of image.
d. Rotating the image : By generating a rotation
matrix.
e. Resizing the image :By using resize function.
f. Drawing a Rectangle : By using rectangle function.
g.Displaying text

18. Region of interest RGB
Drawing a rectangle Rotating a image

19. Reference links
https://www.geeksforgeeks.org/
https://mlait.in/2019/08/26/machine-learning-
libraries-in-python/
https://analyticsindiamag.com/5-reasons-why-
python-is-the-dominant-language-for-
machine-learning/
https://en.wikipedia.org/wiki/Reference_counti
ng#reference_cycle