This document provides an overview of machine learning using Python. It introduces machine learning applications and key Python concepts for machine learning like data types, variables, strings, dates, conditional statements, loops, and common machine learning libraries like NumPy, Matplotlib, and Pandas. It also covers important machine learning topics like statistics, probability, algorithms like linear regression, logistic regression, KNN, Naive Bayes, and clustering. It distinguishes between supervised and unsupervised learning, and highlights algorithm types like regression, classification, decision trees, and dimensionality reduction techniques. Finally, it provides examples of potential machine learning projects.

1. MACHINE LEARNING
USING PYTHON
K. NITHIN BABU
19001A0445
DEPT OF ECE , JNTUACEA

2. CONTENTS
 INTRO TO MACHINE LEARNING AND THEIR APPLICATIONS
 INTRO TO PYTHON ( DATA TYPES , OPERATORS , VARIABLES , STRINGS, DATE & TIME , CONDITIONAL
STATEMENTS , LOOPS,ETC)
 MACHINE LEARNING LIBRARIES ( NUMPY , MATPLOTLIB , PANDAS)
 STATISTICS AND PROBABILITY
 MACHINE LEARNING ALGORITHMS
 PROJECTS ON MACHINE LEARNING

3. MACHINE LEARNING AND APPLICATIONS
 It is a growing technology which enables computers to
learn automatically from past data by building
mathematical models.
 Image recognition , Speech recognition , Email spam
messages filtering etc..

4. INTRO TO PYTHON
VARIABLE:
It is a reserved memory location to store data values.
There are of different types
DATA TYPES :
A data type is a classification of data which tells the compiler or interpreter how the programmer
intends to use the data
• Numeric data types: int, float, complex.
• String data types: str.
• Sequence types: list, tuple, range.
• Mapping data type: dict.
• Boolean type: bool.
Python offers readable and concise codes. Since machine learning and artificial
intelligence involve complex algorithms, the simplicity of Python adds value and enables the creation of reliable
systems.

5. STRINGS:
string is a sequence of characters . It is an immutable
sequence data type.
Ex:
var1 = 'Hello World!’
var2 = "Python Programming"
Accessing Values in Strings:
To access substrings, use the square brackets for slicing along with the
index or indices to obtain your substring.
EX:
var1 = 'Hello World!’
var2 = "Python Programming"

var1 [0] : H
var2 [1:5] : ytho
String Concatenation :
we can join two or more strings using + operator
var1=‘hello’
var2=‘world’
var3=var1+” “+var2
var3  hello world

6. OPERATORS
TYPES OF OPERATORS :
• Arithmetic operators ( + , - , % , / )
• Assignment operators (=)
• Comparison operators ( < , > , == , <= ,>= )
• Logical operators ( and , or , not)
• Bitwise operators ( AND , OR , XOR , NOT )
Operators are used to perform operations on variables and values.

7. DATE AND TIME MODULE
 In Python, date and time are not a data type of their own, but a
module named datetime can be imported to work with the date as
well as time.
Import the datetime module and display the current date:
OUTPUT:
2022-12-08 11:28:08.719413
To Get Today’s Year, Month, and Date:
Current year: 2022 Current month: 12 Current day: 9

8. CONDITIONAL STATEMENTS IN PYTHON:
 Conditional Statement in Python perform different computations or actions depending on whether a specific
Boolean constraint evaluates to true or false
 If Statement
 If else Statement
 elif Statement
If statement:
if statement is how you perform this sort of decision-making. It allows for conditional execution

9. If else statement:
If the condition provided in the if statement is false, then the else
statement will be executed.
SYNTAX:
if <expr>:
<statement(s)>
else:
<statement(s)>

10. Elif Statement:
The elif statement allows you to check multiple expressions for
TRUE and execute a block of code as soon as one of the conditions
evaluates to TRUE.
SYNTAX:
if <expr>:
<statement(s)>
elif:
<statement(s)>
elif:
<statement(s)>

11. LOOPS IN PYTHON:
We can run a single statement or set of statements repeatedly using a
loop command.
TYPES:
for , while , nested loops.
for loop:
A for loop is used for iterating over a
sequence (that is either a list, a tuple, a
dictionary, a set, or a string).
Ex:
fruits=["a", "b", "c"]
for x in fruits:
print(x)
Output:
a
b
c
while loop
With the while loop we can execute a set of statements
as long as a condition is true.
Ex:
i = 1
while i < 4:
print(i)
i += 1
Output:
1
2
3

12. LIST:
List is used to store data of different data types in a sequential manner. There are addresses assigned to
every element of the list, which is called as Index
EX:
my_list = [1, 2, 3, 'example', 3.132]
TUPLE:
A tuple is created by placing all the items (elements) inside parentheses () , separated by commas. It is
immutable.
EX:
my_tuple = (1, "Hello", 3.4)
DICTIONARY:
Dictionaries are used to store key-value pairs.
EX:
my_dict = {1: 'Python', 2: 'Java’}
SET:
Sets are a collection of unordered elements that are unique. Meaning that even if the data is repeated
more than one time, it would be entered into the set only once.
EX:
my_set = {1, 2, 3, 4, 5, 5, 5}

13. MACHINE LEARNING LIBRARIES
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 used for working with arrays. It also has functions for working in domain of linear
algebra, fourier transform, and matrices.
MATPLOTLIB:
Matplotlib is a comprehensive library for creating static, animated, and interactive
visualizations in Python. used for 2D plots of arrays using Numpy arrays.
PANDAS:
Pandas is an open source Python package that is most widely used for data
science/data analysis and machine learning tasks

14. POBABILITY AND STATISTICS IN MACHINE LEARNING
Probability and statistics both are the most important concepts for Machine Learning.
PROBABILITY:
Probability is about predicting the likelihood of future events, while statistics
involves the analysis of the frequency of past events.
Probability can be calculated by the number of times the event occurs divided by the
total number of possible outcomes.
STATISTICS:
Statistics is a core component of data analytics and machine learning. It helps you analyze
and visualize data to find unseen patterns.
EX: Mean, Median , Standard deaviation.

15. Deep Dive into Ml:
Algorithms In ML

16. Types of Ml algorithms

17. Linear regression
 Linear Regression is a machine learning algorithm based on supervised learning. It
performs a regression task. Regression models a target prediction value based on
independent variables. It is mostly used for finding out the relationship between variables
and forecasting.
For example, if a company's sales have
increased steadily every month for the past
few years, by conducting a linear analysis on
the sales data with monthly sales, the
company could forecast sales in future
months

18. Logistic Regression
 Logistic regression is an example of supervised learning. It is used to calculate or predict
the probability of a binary (yes/no) event occurring. An example of logistic regression
could be applying machine learning to determine if a person is likely to be infected with
COVID-19 or not
For example, predicting if an incoming email is
spam or not spam, or predicting if a credit card
transaction is fraudulent or not fraudulent

19. KNN-K Nearest Neighbours
 K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on
Supervised Learning technique
 K-NN algorithm stores all the available data and classifies a new data point based on the
similarity. This means when new data appears then it can be easily classified into a well suite
category by using K- NN algorithm.
Example: we want to know either it is
a cat or dog. So for this identification,
we can use the KNN algorithm, as it
works on a similarity measure.

20. Naïve bayes algorithm
 Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes
theorem and used for solving classification problems.
 simple and most effective Classification algorithms which helps in building the fast machine
learning models that can make quick predictions.
 It is a probabilistic classifier, which means it predicts on the basis of the probability of
an object.
Example:It is used for Credit Scoring.

21. Regression VS classification
 Regression and Classification algorithms are Supervised Learning algorithms.
 Classification: it is a process of finding a function which helps in dividing the data set into
classes based on different parameters
 Regression:Regression is a process of finding the correlations between dependent and
independent variables
 The main difference is Regression and Classification algorithms that Regression used
to predict the continuous values such as price, salary, age, etc. and Classification are
used to predict/Classify the discrete values such as Male or Female, True or False, Spam
or Not Spam, etc.

22. Decision tree
 Decision Tree is a Supervised learning technique that can be used for both classification
and Regression problems
 It is a tree-structured classifier, where internal nodes represent the features of a dataset,
branches represent the decision rules and each leaf node represents the outcome.
Example: Suppose there is a
candidate who has a job offer and
wants to decide whether he should
accept the offer or Not.

23. Clustering
 It is an unsupervised learning method, hence no supervision is provided to the algorithm,
and it deals with the unlabeled dataset
 A way of grouping the data points into different clusters, consisting of similar data
points. The objects with the possible similarities remain in a group that has less or no
similarities with another group
1.Partitioning Clustering
2.Density-Based Clustering
3.Distribution Model-Based Clustering
4.Hierarchical Clustering
5.Fuzzy Clustering
Types of Clustering Methods:

24. DBSCAN
Density-based spatial clustering of applications with noise (DBSCAN) is a data
clustering algorithm It’s well known in the machine learning and data mining community.
DBSCAN has been widely used in both academia and industrial fields such as computer vision,
recommendation systems and bio-engineering.

25. Dimensionality reduction
 The number of input variables or features for a dataset is referred to as its
dimensionality. Dimensionality reduction refers to techniques that reduce
the number of input variables in a dataset.

26. Principal component analysis
Linear dimension analysis
 Principal Component Analysis (PCA) is an unsupervised linear
transformation technique that is widely used across different fields,
most prominently for feature extraction and dimensionality
reduction. Other popular applications of PCA include exploratory
data analyses and de-noising of signals in stock market trading, and
the analysis of genome data and gene expression levels in the field
of bioinformatics.
 Linear discriminant analysis is primarily used here to
reduce the number of features to a more manageable number
before classification. Each of the new dimensions is a linear
combination of pixel values, which form a template

29. Supervised VS Unsupervised
Supervised Machine Learning:
 Supervised learning is a machine learning method in which models are trained
using labeled data. In supervised learning, models need to find the mapping
function to map the input variable (X) with the output variable (Y).
Unsupervised Machine Learning:
 Unsupervised learning is another machine learning method in which patterns
inferred from the unlabeled input data. The goal of unsupervised learning is to find
the structure and patterns from the input data. Unsupervised learning does not need
any supervision. Instead, it finds patterns from the data by its own.

30. PROJECTS:
1.RESTAURENT REVIEW USING NLP
2.WIRELESS SOUND CONTROL
The Volume Control With Hand Detection OpenCV Python was developed using Python OpenCV, In
this Python OpenCV Project With Source Code we are going Building a Volume Controller with OpenCV ,
To change the volume of a computer
This simple project is an online platform where can restaurant owners or management can
published their restaurant information which they can gather some reviews from their
customers. This simple project can help the restaurant management to market or enhance
their services based on the reviews submitted by their customers.