1. Artificial intelligence (AI) refers to simulating human intelligence through machine learning and programming. 2. AI has applications in personalized shopping through recommendations, fraud prevention, education, and AI assistants. 3. There are different types of AI from reactive machines to theoretical self-aware AI not currently possible. Machine learning is a subset of AI that allows systems to learn from data without being explicitly programmed.

1. Artificial Intelligece &
Machine Learning
Project
NAME- ASTHA CHAURASIA
BRANCH – AIML 0901AM221023
SUBMITTED TO –PROF.DR.BHAGAT SINGH
RAGHUWANSHI

2. What Is Artificial Intelligence (AI)?
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are
programmed to think like humans and mimic their actions. The term may also be applied to any
machine that exhibits traits associated with a human mind such as learning and problem-solving.
Application OF AI
1.Personalized Shopping
Artificial Intelligence technology is used to create recommendation engines through which you can engage better with your
customers. These recommendations are made in accordance with their browsing history, preference, and interests. It helps
in improving your relationship with your coustmor.
2.AI-Powered Assistants
Virtual shopping assistants and chatbots help improve the user experience while shopping online.It is used to make the
conversation sound as human and personal as possible. Moreover, these assistants can have real-time engagement with
your customer.
3.Fraud Prevention
Credit card frauds and fake reviews are two of the most significant issues that E-Commerce companies deal with. By
considering the usage patterns, AI can help reduce the possibility of credit card fraud taking place.
4.Education
Although the education sector is the one most influenced by humans, Artificial Intelligence has slowly begun to seep its
roots into the education sector as well. Even in the education sector, this slow transition of Artificial Intelligence has helped
increase productivity among faculties and helped them concentrate more on students than office or administration work.

3. Types of artificial intelligence
1.Reactive machines: Limited AI that only reacts to different kinds of stimuli based on
preprogrammed rules. Does not use memory and thus cannot learn with new data. IBM’s Deep
Blue that beat chess champion Garry Kasparov in 1997 was an example of a reactive machine.
2.Limited memory: Most modern AI is considered to be limited memory. It can use memory to
improve over time by being trained with new data, typically through an artificial neural network or
other training model. Deep learning, a subset of machine learning, is considered limited memory
artificial intelligence.
3.Theory of mind: Theory of mind AI does not currently exist, but research is ongoing into its
possibilities. It describes AI that can emulate the human mind and has decision-making capabilities
equal to that of a human, including recognizing and remembering emotions and reacting in social
situations as a human would.
4.Self aware: A step above theory of mind AI, self-aware AI describes a mythical machine that is
aware of its own existence and has the intellectual and emotional capabilities of a human. Like
theory of mind AI, self-aware AI does not currently exist.
What is MACHINE
LEARNING
Machine Learning is the field of study that gives computers the capability to learn without being
explicitly programmed. ML is one of the most exciting technologies that one would have ever come
across. As it is evident from the name, it gives the computer that makes it more similar to
humans: The ability to learn. Machine learning is actively being used today, perhaps in many more
places than one would expect.

4. APPLICATION OF MACHINE LEARNING
1. Image Recognition:
Image recognition is one of the most common applications of machine learning. It is used to
identify objects, persons, places, digital images, etc. The popular use case of image recognition
and face detection is, Automatic friend tagging suggestion:
Facebook provides us a feature of auto friend tagging suggestion. Whenever we upload a photo
with our Facebook friends, then we automatically get a tagging suggestion with name, and the
technology behind this is machine learning's face detection and recognition algorithm.
It is based on the Facebook project named "Deep Face," which is responsible for face recognition
and person identification in the picture.
2. Speech Recognition
While using Google, we get an option of "Search by voice," it comes under speech recognition, and
it's a popular application of machine learning.
Speech recognition is a process of converting voice instructions into text, and it is also known as
"Speech to text", or "Computer speech recognition." At present, machine learning algorithms are
widely used by various applications of speech recognition. Google assistant, Siri, Cortana,
and Alexa are using speech recognition technology to follow the voice instructions.
3. Traffic prediction:
If we want to visit a new place, we take help of Google Maps, which shows us the correct path with
the shortest route and predicts the traffic conditions.

5. Types of Machine Learning
1. Supervised Machine Learning
As its name suggests, Supervised machine learning is based on supervision. It means in the
supervised learning technique, we train the machines using the "labelled" dataset, and based on
the training, the machine predicts the output. Here, the labelled data specifies that some of the
inputs are already mapped to the output. More preciously, we can say; first, we train the machine
with the input and corresponding output, and then we ask the machine to predict the output using
the test dataset.
2. Unsupervised Machine Learning
Unsupervised learning is different from the Supervised learning technique; as its name suggests,
there is no need for supervision. It means, in unsupervised machine learning, the machine is
trained using the unlabeled dataset, and the machine predicts the output without any supervision.

6. 4. Reinforcement Learning
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. Agent gets rewarded for
each good action and get punished for each bad action; hence the goal of reinforcement learning
agent is to maximize the rewards.