Introduction to Artificial Intelligence describing domains of AI including machine learning , deep learning , natural language processing , speech recognition.

1. Machine Learning || Deep Learning || Artificial
Intelligence
AI DOMAIN

2. Relation between AI || ML|| DL
 AI means getting a computer to mimic human
behaviour in some way.
 Machine learning is a subset of AI, and it
consists of the techniques that enable computers
to figure things out from the data and deliver AI
applications.
 Deep learning, meanwhile, is a subset of
machine learning that enables computers to solve
more complex problems.

3. Relation between AI || ML|| DL

4. Artificial Intelligence

5. Subsets of Artificial Intelligence
Following are the most common subsets of AI:
 Machine Learning
 Deep Learning
 Natural Language processing
 Robotics
 Machine Vision
 Speech Recognition

6. About 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. We can define it in a
summarized way as:
“ Machine learning enables a machine to
automatically learn from data, improve
performance from experiences, and predict things
without being explicitly programmed. “

7. How does Machine Learning work

8. Types of Machine Learning

9. Supervised Learning
 Supervised learning is a type of 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 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.

10. 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.

11. Reinforcement Learning
 Reinforcement learning is a 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 robotic dog, which automatically learns the
movement of his arms, is an example of
Reinforcement learning.

12. Deep Learning
 . Deep learning is a machine learning technique
that teaches computers to do what comes
naturally to humans: learn by example.
 Deep learning is a key technology behind
driverless cars, enabling them to recognize a stop
sign, or to distinguish a pedestrian from a
lamppost.

13. Examples of Deep Learning
 Automated Driving: Automotive researchers are using
deep learning to automatically detect objects such as
stop signs and traffic lights.
 Aerospace and Defence: Deep learning is used to
identify objects from satellites that locate areas of
interest, and identify safe or unsafe zones for troops.
 Medical Research: Cancer researchers are using
deep learning to automatically detect cancer cells.
 Industrial Automation: Deep learning is helping to
improve worker safety around heavy machinery by
automatically detecting when people or objects are
within an unsafe distance of machines.

14. Natural Language processing
 Natural language processing is a subfield of computer
science and artificial intelligence. NLP enables a
computer system to understand and process human
language such as English.
 NLP plays an important role in AI as without NLP, AI
agent cannot work on human instructions, but with the
help of NLP, we can instruct an AI system on our
language. Today we are all around AI, and as well as
NLP, we can easily ask Siri, Google or Cortana to
help us in our language.
The Input and output of NLP applications can be in
two forms:
 Speech
 Text

15. Machine Vision
 Machine vision is an application of computer
vision which enables a machine to recognize the
object.
 Machine vision captures and analyses visual
information using one or more video cameras,
analog-to-digital conversations, and digital signal
processing.
 Machine vision systems are programmed to
perform narrowly defined tasks such as counting
objects, reading the serial number, etc.

16. Speech Recognition
 Speech recognition is a technology which
enables a machine to understand the spoken
language and translate into a machine-readable
format.
 It is a way to talk with a computer, and on the
basis of that command, a computer can perform a
specific task.
Speech recognition systems can be used in the
following areas:
 System control or navigation system
 Industrial application
 Voice dialing system