Ml Fundamentals and applications using python

Course Name: Machine Learning
Course Code: 201CS6T01
By
Ms.T.Sudha Rani
Assoc.Professor
Dept of Computer Science and Engineering
Aditya Engineering College(A)
Surampalem.
T.Sudha Rani Assoc.Professor

UNIT I
Introduction to Machine
Learning

Contents:
 Artificial Intelligence
 Machine Learning
 Deep Learning
 Types of Machine Learning Systems
 Main Challenges of Machine Learning
 Statistical Learning

 Artificial Intelligence
 Artificial intelligence is a wide-ranging branch of computer
science concerned with building smart machines capable of
performing tasks that typically require human intelligence.
 Artificial Intelligence is composed of two words Artificial and
Intelligence, where Artificial defines "man-made," and
intelligence defines "thinking power", hence AI means "a man-
made thinking power."

Goals Of Artificial
Intelligence:
Following are the main goals of Artificial Intelligence:
1. Replicate human intelligence
2. Solve Knowledge-intensive tasks
3. An intelligent connection of perception and action
4. Building a machine which can perform tasks that requires
human intelligence such as: o Proving a theorem o Playing
chess o Plan some surgical operation o Driving a car in traffic
5. Creating some system which can exhibit intelligent behavior,
learn new things by itself, demonstrate, explain, and can advise
to its user.

Advantages of Artificial Intelligence:
Following are some main advantages of Artificial Intelligence:
o High Accuracy with less errors: AI machines or systems are
prone to less errors and high accuracy as it takes decisions as
per pre-experience or information.
o High-Speed: AI systems can be of very high-speed and fast-
decision making, because of that AI systems can beat a chess
champion in the Chess game.
 High reliability: AI machines are highly reliable and can
perform the same action multiple times with high accuracy.
o Useful for risky areas: AI machines can be helpful in situations
such as defusing a bomb, exploring the ocean floor, where to
employ a human can be risky.

Advantages of Artificial Intelligence:
o Digital Assistant: AI can be very useful to provide digital
assistant to the users such as AI technology is currently used by
various E-commerce websites to show the products as per
customer requirement.
o Useful as a public utility: AI can be very useful for public
utilities such as a selfdriving car which can make our journey
safer and hassle-free, facial recognition for security purpose,
Natural language processing to communicate with the human in
human-language, etc.

Disadvantages Of Artificial Intelligence:
Every technology has some disadvantages, and the same goes for
Artificial intelligence. Being so advantageous technology still, it
has some disadvantages which we need to keep in our mind
while creating an AI system. Following are the disadvantages of
AI:
High Cost: The hardware and software requirement of AI is very
costly as it requires lots of maintenance to meet current world
requirements. o Can't think out of the box: Even we are making
smarter machines with AI, but still they cannot work out of the
box, as the robot will only do that work for which they are
trained, or programmed.

Disadvantages Of Artificial Intelligence:
No feelings and emotions: AI machines can be an outstanding
performer, but still it does not have the feeling so it cannot make
any kind of emotional attachment with human, and may
sometime be harmful for users if the proper care is not taken.
Increase dependency on machines: With the increment of
technology, people are getting more dependent on devices and
hence they are losing their mental capabilities.
No Original Creativity: As humans are so creative and can
imagine some new ideas but still AI machines cannot beat this
power of human intelligence and cannot be creative and
imaginative.

History of Machine
Learning

Fundamentals of Machine Learning:
 Machine learning is a growing technology which
enables computers to learn automatically from past
data.
 Machine learning uses various algorithms for building
mathematical models and making predictions using
historical data or information.
 Currently, it is being used for various tasks such as
image recognition, speech recognition, email filtering,
Facebook auto-tagging, recommender system, and
many more.

In the real world, we are surrounded by humans
who can learn everything from their experiences
with their learning capability, and we have
computers or machines which work on our
instructions. But can a machine also learn from
experiences or past data like a human does?
So here comes the role of Machine Learning.
Machine Learning
.

Machine Learning is said as a subset of artificial
intelligence that is mainly concerned with the
development of algorithms which allow a computer to
learn from the data and past experiences on their own.
The term machine learning was first introduced by
Arthur Samuel in 1959.
Machine learning enables a machine to automatically
learn from data, improve performance from
experiences, and predict things without being
explicitly programmed.

 With the help of sample historical data, which is known
as training data, machine learning algorithms build a
mathematical model that helps in making predictions or
decisions without being explicitly programmed.
 Machine learning brings computer science and statistics
together for creating predictive models.
 Machine learning constructs or uses the algorithms that
learn from historical data.
 The more we will provide the information, the higher
will be the performance.

A machine has the ability to learn if it can
improve its performance by gaining more
data.

How does Machine Learning work !!!
 A Machine Learning system learns from historical data,
builds the prediction models, and whenever it receives
new data, predicts the output for it.
 The accuracy of predicted output depends upon the amount
of data, as the huge amount of data helps to build a better
model which predicts the output more accurately.

Features of Machine Learning:
 Machine learning uses data to detect various
patterns in a given dataset.
 It can learn from past data and improve
automatically.
 It is a data-driven technology.
 Machine learning is much similar to data mining
as it also deals with the huge amount of the
data.

importance of Machine Learning:
• Rapid increment in the production of data
• Solving complex problems, which are difficult
for a human
• Decision making in various sector including
finance
• Finding hidden patterns and extracting useful
information from data.

Machine learning life cycle involves
seven major steps, which are given
below:
•Gathering Data
•Data preparation
•Data Wrangling
•Analyse Data
•Train the model
•Test the model
•Deployment

 Machine learning is a subset of AI,
which enables the machine to
automatically learn from data, improve
performance from past experiences, and
make predictions.
 Machine learning contains a set of
algorithms that work on a huge amount of
data. Data is fed to these algorithms to train
them, and on the basis of training, they
build the model & perform a specific task.

FOUR BRANCHES OF ML

Machine learning is divided into mainly four
types, which are:
1.Supervised Machine Learning
2.Unsupervised Machine Learning
3.Semi-Supervised Machine Learning
4.Reinforcement Learning

Supervised Learning
• Machine learning method in which we provide sample labeled data to the
machine learning system in order to train it, and on that basis, it predicts
the output.
• The system creates a model using labeled data to understand the
datasets and learn about each data, once the training and processing are
done then we test the model by providing a sample data to check whether
it is predicting the exact output or not.
• The goal of supervised learning is to map input data with the output data.
The supervised learning is based on supervision, and it is the same as
when a student learns things in the supervision of the teacher. The
example of supervised learning is spam filtering.

Supervised learning can be grouped
further in two categories of algorithms:
• Classification
• Regression

Classification :Classification algorithms are used to solve the classification
problems in which the output variable is categorical, such as "Yes" or No,
Male or Female, Red or Blue, etc. The classification algorithms predict the
categories present in the dataset. Some real-world examples of
classification algorithms are Spam Detection, Email filtering, etc.
Some popular classification algorithms are given below:
•Random Forest Algorithm
•Decision Tree Algorithm
•Logistic Regression Algorithm
•Support Vector Machine Algorithm

Regression
Regression algorithms are used to solve
regression problems in which there is a
linear relationship between input and output
variables. These are used to predict
continuous output variables, such as market
trends, weather prediction, etc.
Some popular Regression algorithms are
given below:
•Simple Linear Regression Algorithm
•Multivariate Regression Algorithm
•Decision Tree Algorithm
•Lasso Regression

Advantages and Disadvantages of Supervised Learning
Advantages:
•Since supervised learning work with the labelled dataset so
we can have an exact idea about the classes of objects.
•These algorithms are helpful in predicting the output on the
basis of prior experience.
Disadvantages:
•These algorithms are not able to solve complex tasks.
•It may predict the wrong output if the test data is different
from the training data.
•It requires lots of computational time to train the algorithm.

Unsupervised Learning
Unsupervised learning is a learning method in which a machine
learns without any supervision.
The training is provided to the machine with the set of data that has
not been labeled, classified, or categorized, and the algorithm needs
to act on that data without any supervision.
The goal of unsupervised learning is to restructure the input data
into new features or a group of objects with similar patterns.

Unsupervised Learning

• In unsupervised learning, we don't have a
predetermined result.
• The machine tries to find useful insights from
the huge amount of data.
It can be further classifieds into two categories of
algorithms:
 Clustering
 Association

•Clustering: Clustering is a method of grouping the objects
into clusters such that objects with most similarities
remains into a group and has less or no similarities with the
objects of another group. Cluster analysis finds the
commonalities between the data objects and categorizes
them as per the presence and absence of those
commonalities.
•Association: An association rule is an unsupervised
learning method which is used for finding the relationships
between variables in the large database. It determines the
set of items that occurs together in the dataset. Association
rule makes marketing strategy more effective. Such as
people who buy X item (suppose a bread) are also tend to
purchase Y (Butter/Jam) item. A typical example of
Association rule is Market Basket Analysis.

Unsupervised Learning algorithms:
Below is the list of some popular unsupervised learning
algorithms:
•K-means clustering
•KNN (k-nearest neighbors)
•Hierarchal clustering
•Anomaly detection
•Neural Networks
•Principle Component Analysis
•Independent Component Analysis
•Apriori algorithm
•Singular value decomposition

Advantages and Disadvantages of Unsupervised Learning Algorithm
Advantages:
•These algorithms can be used for complicated tasks compared to the
supervised ones because these algorithms work on the unlabeled dataset.
•Unsupervised algorithms are preferable for various tasks as getting the
unlabeled dataset is easier as compared to the labeled dataset.
Disadvantages:
•The output of an unsupervised algorithm can be less accurate as the
dataset is not labeled, and algorithms are not trained with the exact output
in prior.
•Working with Unsupervised learning is more difficult as it works with the
unlabeled dataset that does not map with the output.

Semi-Supervised learning :It represents the
intermediate ground between Supervised (With
Labeled training data) and Unsupervised (with no
labeled training data) algorithms.
uses the combination of labeled and unlabeled
datasets during the training period.
It operates on the data that consists of a few labels, it
mostly consists of unlabeled data.

 As labels are costly, but for corporate purposes, they
may have few labels.
 It is completely different from supervised and
unsupervised learning as they are based on the
presence & absence of labels.
 To overcome the drawbacks of supervised
learning and unsupervised learning algorithms,
the concept of Semi-supervised learning is
introduced.

Advantages:
•It is simple and easy to understand the algorithm.
•It is highly efficient.
•It is used to solve drawbacks of Supervised and
Unsupervised Learning algorithms.
Disadvantages:
•Iterations results may not be stable.
•We cannot apply these algorithms to network-level data.
•Accuracy is low.

• Reinforcement learning works on a feedback-based process, in which an
AI agent (A software component) automatically explore its surrounding
by hitting & trail, taking action, learning from experiences, and
improving its performance.
• In reinforcement learning, there is no labelled data like supervised learning,
and agents learn from their experiences only.
• The reinforcement learning process is similar to a human being; for example,
a child learns various things by experiences in his day-to-day life.

A reinforcement learning problem can be formalized
using Markov Decision Process(MDP).
In MDP, the agent constantly interacts with the
environment and performs actions; at each action, the
environment responds and generates a new state.

Reinforcement learning is categorized mainly into two types of
methods/algorithms:
•Positive Reinforcement Learning: Positive reinforcement learning
specifies increasing the tendency that the required behaviour would
occur again by adding something. It enhances the strength of the
behaviour of the agent and positively impacts it.
•Negative Reinforcement Learning: Negative reinforcement learning
works exactly opposite to the positive RL. It increases the tendency that
the specific behaviour would occur again by avoiding the negative
condition.

Advantages
•It helps in solving complex real-world problems which are
difficult to be solved by general techniques.
•The learning model of RL is similar to the learning of human
beings; hence most accurate results can be found.
•Helps in achieving long term results.
Disadvantage
•RL algorithms are not preferred for simple problems.
•RL algorithms require huge data and computations.
•Too much reinforcement learning can lead to an overload of
states which can weaken the results.

Clustering
When we want to find the inherent groups from the data.
It is a way to group the objects into a cluster such that the objects with
the most similarities remain in one group and have fewer or no
similarities with the objects of other groups. Some of the popular
clustering algorithms are given below:
•K-Means Clustering algorithm
•Mean-shift algorithm
•DBSCAN Algorithm
•Principal Component Analysis
•Independent Component Analysis

Reinforcement Learning :
 Feedback-based learning method, in which a learning
agent gets a reward for each right action and gets a
penalty for each wrong action.
 The agent learns automatically with these feedbacks and
improves its performance.
 The agent interacts with the environment and explores it.
 The goal of an agent is to get the most reward points,
and hence, it improves its performance.
 The robotic dog, which automatically learns the movement
of his arms, is an example of Reinforcement learning.

 Evaluating Machine Learning models is the last stage before
deploying a model to production.
 We evaluate Machine Learning models to confirm that they are
performing as expected and that they are good enough for the task
they were created for.
 The evaluation stage is performed after model training is finished.
Different techniques are used depending on the type of problem
and type of algorithm.
 Most evaluation techniques rely on comparing the training data
with test data that was split from the original training data.
 This only works if both the training data as a whole and the test
data are representative of the real world data.

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