This document provides an overview of machine learning. It defines machine learning as a type of artificial intelligence that allows systems to learn and improve automatically through experience without being explicitly programmed. The document then discusses machine learning tasks, reasons for implementing it, types of machine learning algorithms including supervised, unsupervised and semi-supervised, real-life applications, advantages such as identifying patterns and continuous improvement, and disadvantages including the need for large datasets and potential for high error susceptibility.

1. MACHINE LEARNING
PRESENTED BY:
MAHEEN DILAWAR
ROLL NO: 041
4TH SEMESTER
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2. DEFINITION
 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.
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3. INTRODUCTION
 Arthur Samuel first came up with the phrase
“Machine Learning” in 1952.
 In 1957, Frank Rosenblatt at the Cornell
Aeronautical Laboratory combined Donald Hebb's
model of brain cell interaction with Arthur
Samuel's Machine Learning efforts and created the
perceptron.
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4. TASKS PERFORMED BY
MACHINE LEARNING
1) Finding, extracting and summarizing relevant data
2) Making predictions based on the analysis data
3) Calculating probabilities for specific results
4) Adapting to certain developments autonomously
5) Optimizing processes based on recognized
patterns
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5. REASONS TO IMPLEMENT
MACHINE LEARNING
 There reasons why implementing machine learning
can be useful are as follows:
1) It can help us to understand the inner works of an
algorithm
2) We could try to implement an algorithm more
efficiently
3) We can add new features to an algorithm or
experiment with different variations of the core
idea
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4) We circumvent licensing issues (e.g., Linux vs.
Unix) or platform restrictions
5) We want to invent new algorithms or implement
algorithms no one has implemented/shared yet
6) We are not satisfied with the API and/or we want
to integrate it more "naturally" into an existing
software library
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7. REAL TIME MACHINE
LEARNING (RTML)
 A grand challenge in computing is the creation of
machines that can proactively interpret and learn
from data in real time, solve problems and operate
with the energy efficiency of the human brain.
 The National Science Foundation and the Defense
Advanced Research Projects Agency are teaming
up through this (RTML) program to explore high
performance, energy efficient hardware and
machine learning architectures that can learn from
a continuous stream of new data in real time.
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8. MACHINE LEARNING METHODS
1) Supervised Machine Learning Algorithms: They can
apply what has been learned in the past to new data using
labeled examples to predict future events. Starting from the
analysis of a known training dataset, the learning algorithm
produces an inferred function to make predictions about the
output values. The system is able to provide targets for any
new input after sufficient training.
2) Reinforcement Machine Learning Algorithms: It is a
learning method that interacts with its environment by
producing actions and discovers errors or rewards
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3) Unsupervised Machine Learning Algorithms:
They are used when the information used to train
is neither classified nor labeled. Unsupervised
learning studies how systems can infer a function
to describe a hidden structure from unlabeled data.
The system doesn’t figure out the right output, but
it explores the data and can draw inferences from
datasets to describe hidden structures from
unlabeled data.
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4) Semi-supervised Machine Learning Algorithms:
They fall somewhere in between supervised and
unsupervised learning, since they use both labeled
and unlabeled data for training. The systems that
use this method are able to considerably improve
learning accuracy. Usually, semi-supervised
learning is chosen when the acquired labeled data
requires skilled and relevant resources in order to
train it / learn from it.
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11. REAL LIFE EXAMPLES OF
MACHINE LEARNING
1) Image Recognition: Machine learning can be used
for face detection in an image as well.
2) Speech Recognition: Speech recognition is used
in the applications like voice user interface, voice
searches and more
3) Medical Diagnosis: Machine learning can be used
in the techniques and tools that can help in
the diagnosis of diseases.
4) Statistical Arbitrage: Machine learning methods
are applied to obtain an index arbitrage strategy.
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5) Learning Associations: One of the applications of
machine learning is studying the associations
between the products that people buy.
6) Classification: Classification helps to analyze the
measurements of an object to identify the category
to which that object belongs.
7) Prediction: Machine learning can also be used in
the prediction systems.
8) Extraction: It is the process of extracting
structured information from the unstructured data.
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9) Regression: In regression, we can use the
principle of machine learning to optimize the
parameters.
10)Financial Services: Machine learning can help the
banks, financial institutions to make smarter
decisions
 Conclusion: We can say that machine learning
is an incredible breakthrough in the field of
artificial intelligence.
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14. ADVANTAGES OF MACHINE
LEARNING
1) Easily Identifies Trends And Patterns: Machine
Learning can review large volumes of data and
discover specific trends and patterns that would
not be apparent to humans. For instance, for an e-
commerce website like Amazon, it serves to
understand the browsing behaviors and purchase
histories of its users to help cater to the right
products, deals, and reminders relevant to them. It
uses the results to reveal relevant advertisements to
them.
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2) No Human Intervention Needed (Automation):
With ML, you don’t need to babysit your project
every step of the way. Since it means giving
machines the ability to learn, it lets them make
predictions and also improve the algorithms on
their own.
3) Continuous Improvement: As ML algorithms
gain experience, they keep improving in accuracy
and efficiency. This lets them make better
decisions.
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4) Handling Multi-dimensional And Multi-variety
Data: Machine Learning algorithms are good at
handling data that are multi-dimensional and
multi-variety, and they can do this in dynamic or
uncertain environments.
5) Wide Applications: You could be an e-tailer or a
healthcare provider and make ML work for you.
Where it does apply, it holds the capability to help
deliver a much more personal experience to
customers while also targeting the right customers.
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17. DISADVANTAGES OF MACHINE
LEARNING
1) Data Acquisition: Machine Learning requires
massive data sets to train on, and these should be
inclusive/unbiased, and of good quality. There can
also be times where they must wait for new data to
be generated.
2) Interpretation of Results: Another major
challenge is the ability to accurately interpret
results generated by the algorithms. You must also
carefully choose the algorithms for your purpose.
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3) Time and Resources: ML needs enough time to
let the algorithms learn and develop enough to
fulfill their purpose with a considerable amount of
accuracy and relevancy. It also needs massive
resources to function. This can mean additional
requirements of computer power for you.
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4) High Error-Susceptibility: Machine Learning is
autonomous but highly susceptible to errors.
Suppose you train an algorithm with data sets
small enough to not be inclusive. You end up with
biased predictions coming from a biased training
set. This leads to irrelevant advertisements being
displayed to customers. In the case of ML, such
blunders can set off a chain of errors that can go
undetected for long periods of time.
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