The document provides an overview of a Python with Machine Learning internship presentation. It begins with an introduction to Python, describing its origins and features. It then discusses Python operators and flow control. The document defines machine learning and compares it to traditional programming. It outlines the main types of machine learning - supervised, unsupervised, reinforcement, and semi-supervised learning - and describes models based on algorithm outputs. Finally, it provides more detailed explanations of supervised and unsupervised learning, including their categories, computational complexity, accuracy, and provides references used in the presentation.

1. SIR M.VISVESWARAYA INSTITUTE OF TECHNOLOGY
INTERNSHIP PRESENTATION
ON
“
PYTHON WITH MACHINE LEARNING”
Presented By:
KRISHNAVENI
1MV18EC054
8th sem ECE , ’A’Sec
Under the guidance of:
Mrs. Seema S
Assistant professor,Dept of ECE
SIR MVIT,Banglore
SIR MVIT, Banglore

2. PYTHON
Introduction:
Python is a general purpose high level programming language.
Python was developed by Guido Van Rossam in 1989 while working
at National Research Institute at Netherlands.
But officially Python was made available to public in 1991. The
official Date of Birth for Python is: Feb 20th 1991.
Python is recommended as first programming language for
beginners.

3. Features of Python:
Simple and easy to learn
Freeware and Open Source
High Level Programming language
Both Procedure Oriented and Object Oriented
Limitations of Python:
Performance wise not up to the mark b'z it is interpreted language.
Not using for mobile Applications.

4. Python Operators:
The operator can be defined as a symbol which is responsible for a
particular operation between two operands. Operators are the
pillars of a program on which the logic is built in a particular
programming language. Python provides a variety of operators
described as follows.
Arithmetic Operators
Relational Operators or Comparison Operators
Logical operators
Bitwise operators
Assignment operators
Special operators

5. Flow Control:
Flow control describes the order in which statements will be
executed at runtime.

6. What is Machine Learning?
Machine Learning is a system that can learn from example
through self improvement and without being explicitly coded by
programmer. The breakthrough comes with the idea that a
machine can singularly learn from the data (i.e., example)
produce accurate results.
Machine learning combines data with statistical tools to predict
an output.
The machine receives data as input, use an algorithm to formulate
answers.

7. Machine Learning vs. Traditional Programming
Traditional programming differs significantly from machine learning. In
traditional programming, a programmer codes all the rules in consultation with an
expert in the industry for which software is being developed. Each rule is based
on a logical foundation; the machine will execute an output following the logical
statement. When the system grows complex, more rules need to be written. It can
quickly become unsustainable to maintain.

8. Machine learning is supposed to overcome this issue. The machine learns how
the input and output data are correlated and it writes a rule. The programmers
do not need to write new rules each time there is new data. The algorithms
adapt in response to new data and experiences to improve efficacy over time.

9. Types f Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Machine Learning
Semi-Supervised Machine Learning

10. Models based on the Kind of
Outputs from the Algorithms

11. Supervised learning
Supervised learning as the name indicates the presence of a supervisor as a teacher.
Basically supervised learning is a learning in which we teach or train the machine
using data which is well labeled that means some data is already tagged with the
correct answer. After that, the machine is provided with a new set of examples(data)
so that supervised learning algorithm analyses the training data(set of training
examples) and produces a correct outcome from labeled data.
Supervised learning classified into two categories of algorithms:
Classification: A classification problem is when the output variable is a category, such as
“Red” or “blue” or “disease” and “no disease”.
Regression: A regression problem is when the output variable is a real value, such as
“dollars” or “weight”.

12. Unsupervised learning
Unsupervised learning is the training of machine using information that is neither
classified nor labelled and allowing the algorithm to act on that information without
guidance. Here the task of machine is to group unsorted information according to
similarities, patterns and differences without any prior training of data.
Unsupervised learning classified into two categories of algorithms:
 Clustering: A clustering problem is where you want to discover the inherent
groupings in the data, such as grouping customers by purchasing behaviour.
 Association: An association rule learning problem is where you want to
discover rules that describe large portions of your data, such as people that buy
X also tend to buy Y.

13. Supervised vs. Unsupervised Machine Learning
Parameters Supervised machine
learning technique
Unsupervised
machine learning
technique
Input Data Algorithms are trained using
labelled data.
Algorithms are used against
data which is not labelled
Computational Complexity Supervised learning is a
simpler method.
Unsupervised learning is
computationally complex
Accuracy Highly accurate and
trustworthy method.
Less accurate and trustworthy
method.

14. References:
https://www.wikipedia.org/
https://data-flair.training/
https://www.analyticsvidhya.com/
https://www.tutorialspoint.com/

16. THANK YOU