INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
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Unit 1 of the document introduces artificial intelligence and machine learning. It discusses how AI solves real-world problems by simulating human intelligence and modeling problem-solving processes. It also covers machine learning models like supervised, unsupervised, and reinforcement learning. Additionally, it introduces popular Python libraries for artificial intelligence like NumPy, Pandas, scikit-learn, and TensorFlow. The role of Python in AI is also discussed along with Anaconda and how to install Python libraries.
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INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
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Unit 1:
Principles of Artificial Intelligence - Introduction - Solving Real World Problems
using AI - Diversity of Disciplines - Fields and Applications of Artificial Intelligence
– Simulating Intelligence - The Turing Test - AI Tools and Learning Models -
Classification and Prediction. Learning Models - The Role of Python in Artificial
Intelligence - AI and Python - Anaconda in Python - Python Libraries for Artificial
Intelligence - Introduction to the NumPy Library.
3. AI-ARTIFICIAL INTELLIGENCE
• Artificial Intelligence (AI) is a science
that's used to construct intelligence
using hardware and software solutions.
• It is inspired by reverse engineering, for
example, in the way that neurons work
in the human brain
10. Machine Learning
• Machine learning is a term that is often confused
with artificial intelligence. It originates from the
1950s, and it was first defined by Arthur Lee
Samuel in 1959.
• Machine learning is a field of study concerned with
giving computers the ability to learn without being
explicitly programmed.
• Machine learning is one way to achieve artificial
intelligence.
• You can have artificial intelligence without
machine learning.
11. How does AI Solve Real World Problems?
• Artificial intelligence automates human
intelligence based on the way human
brain processes information.
• Whenever we solve a problem or
interact with people, we go through a
process. Whenever we limit the scope
of a problem or interaction, this
process can often be modeled and
automated.
12. AI makes computers appear to think
like humans.
Sometimes, it feels like AI knows what we need.
Just think about the personalized coupons you
receive after shopping online. By the end of this
book,
you will understand that to choose the most
successful products, you need to be shown how to
maximize your purchases – this is a relatively simple
task.
However, it is also so efficient, that we often
think that computers "know" what we need.
13. AI is performed by computers that are
executing low-level instructions.
Even though a solution may appear to be
intelligent, we write code, just like with any other
software solutions. Even if we are simulating neurons,
simple machine code and computer hardware executes
the "thinking" process.
Most AI applications have one primary objective.
When we interact with an AI application, it seems
human-like because it can restrict a problem domain to
a primary objective. Therefore, we get a chance to
break down complex processes and simulate
intelligence with the help of low-level computer
instructions.
14. AI may stimulate human senses and
thinking processes for specialized fields.
We must simulate human senses and thoughts, and
sometimes trick AI into believing that we are interacting
with another human. In special cases, we can even
enhance our own senses.
Similarly, when we interact with a chatbot, we expect
the bot to understand us. We expect the chatbot or even a
voice recognition system to provide a computer-human
interface that fulfills our expectations. In order to meet
these expectations, computers need to emulate the
human thought processes.
15. Diversity of Disciplines
• The AI agent needs to process and sense what
is around it in order to drive the car.
• Without understanding the physics of moving
objects, driving the car in a normal
environment would be an almost impossible,
not to mention deadly, task.
• In order to create a usable AI solution, different
disciplines are involved. For example:
• Robotics: To move objects in space Algorithm
16. • Theory: To construct efficient algorithms
• Statistics:To derive useful results, predict
the future, and analyze the past
• Psychology: To model how the human brain
works
• Software Engineering: To create
maintainable solutions that endure the test
of time
• Computer Science or Computer
Programming: To implement our software
Solutions in practice.
17. • Mathematics: To perform complex
Mathematical operations
• Control Theory: To create feedForward and
feedback systems
• Information Theory: To represent, encode,
decode, and compress information
• Graph Theory: To model and optimize
different points in space and to represent
hierarchies
• Physics: To model the real world Computer
Graphics and Image Processing to display and
process images and movies
• Computer Graphics and Image Processing: to
display and process images and movies
18. Fields and Applications of Artificial
Intelligence
• Simulation of Human Behavior Humans have five basic
senses simply divided into visual, auditory, kinesthetic,
olfactory, and gustatory. However, for the purposes of
understanding how to create intelligent machines, we
can separate disciplines as follows:
• Listening and speaking
• Understanding language
• Remembering things
• Thinking
• Seeing
• Moving
20. 1. AI in Astronomy
● Artificial Intelligence can be very useful to solve complex universe
problems. AI technology can be helpful for understanding the universe
such as how it works, origin, etc.
2. AI in Healthcare
● In the last, five to ten years, AI becoming more advantageous for the
healthcare industry and going to have a significant impact on this industry.
● Healthcare Industries are applying AI to make a better and faster diagnosis
than humans. AI can help doctors with diagnoses and can inform when
patients are worsening so that medical help can reach to the patient
before hospitalization.
3. AI in Gaming
● AI can be used for gaming purpose. The AI machines can play strategy
games like chess, where the machine needs to think of a large number of
possible places.
4. AI in Finance
● AI and finance industries are the best matches for each other. The finance
industry is implementing automation, chatbot, adaptive intelligence,
algorithm trading, and machine learning into financial processes.
21. 5. AI in Data Security
● The security of data is crucial for every company and cyber-attacks are growing very rapidly in the
digital world. AI can be used to make your data more safe and secure. Some examples such as
AEG bot, AI2 Platform,are used to determine software bug and cyber-attacks in a better way.
6. AI in Social Media
● Social Media sites such as Facebook, Twitter, and Snapchat contain billions of user profiles,
which need to be stored and managed in a very efficient way. AI can organize and manage
massive amounts of data. AI can analyze lots of data to identify the latest trends, hashtag, and
requirement of different users.
7. AI in Travel & Transport
● AI is becoming highly demanding for travel industries. AI is capable of doing various travel
related works such as from making travel arrangement to suggesting the hotels, flights, and best
routes to the customers. Travel industries are using AI-powered chatbots which can make
human-like interaction with customers for better and fast response.
8. AI in Automotive Industry
● Some Automotive industries are using AI to provide virtual assistant to their user for better
performance. Such as Tesla has introduced TeslaBot, an intelligent virtual assistant.
● Various Industries are currently working for developing self-driven cars which can make your
journey more safe and secure.
22. 9. AI in Robotics:
● Artificial Intelligence has a remarkable role in Robotics. Usually, general robots are programmed
such that they can perform some repetitive task, but with the help of AI, we can create intelligent
robots which can perform tasks with their own experiences without pre-programmed.
● Humanoid Robots are best examples for AI in robotics, recently the intelligent Humanoid robot
named as Erica and Sophia has been developed which can talk and behave like humans.
10. AI in Entertainment
● We are currently using some AI based applications in our daily life with some entertainment
services such as Netflix or Amazon. With the help of ML/AI algorithms, these services show the
recommendations for programs or shows.
11. AI in Agriculture
● Agriculture is an area which requires various resources, labor, money, and time for best result.
Now a day's agriculture is becoming digital, and AI is emerging in this field. Agriculture is
applying AI as agriculture robotics, solid and crop monitoring, predictive analysis. AI in agriculture
can be very helpful for farmers.
23. 12. AI in E-commerce
● AI is providing a competitive edge to the e-commerce industry, and it is becoming
more demanding in the e-commerce business. AI is helping shoppers to discover
associated products with recommended size, color, or even brand.
13. AI in education:
● AI can automate grading so that the tutor can have more time to teach. AI chatbot
can communicate with students as a teaching assistant.
● AI in the future can be work as a personal virtual tutor for students, which will be
accessible easily at any time and any place.
31. Simulating Intelligence – The Turing
Test
• Alan Turing, the inventor of the Turing
machine
• The Turing Test is a method of inquiry
in artificial intelligence (AI) for
determining whether or not a
computer is capable of thinking like a
human being.
32. • Imagine a game of three
players having two humans and
one computer, an
interrogator(as a human) is
isolated from the other two
players.
• The interrogator’s job is to try
and figure out which one is
human and which one is a
computer by asking questions
from both of them. To make
things harder computer is
trying to make the interrogator
guess wrongly.
• In other words, computers
would try to be
indistinguishable from humans
as much as possible.
33. What disciplines do we need to pass the
Turing test?
• Understand a spoken language using
NLP
• Expert on things
Passing the Turing test is very hard.if this
is not enough, we can advance to the
Total Turing Test, which also includes
movement and vision.
34. AI Tools and Learning Models
Intelligent Agents
When solving AI problems, we create an actor in
the environment that can gather data from its
surroundings and influence its surroundings.
This actor is called an intelligent agent.
An intelligent agent:
• Is autonomous
• Observes its surroundings through sensors
• Acts in its environment using actuators
• Directs its activities toward achieving goals
35. AI Tools and Learning Models
AI Learning Models: Knowledge-Based Classification
Factoring its representation of knowledge, AI learning models can be classified in two main
types: inductive and deductive.
—Inductive Learning: This type of AI learning model is based on inferring a general rule from
datasets of input-output pairs.. Algorithms such as knowledge based inductive
learning(KBIL) are a great example of this type of AI learning technique. KBIL focused on
finding inductive hypotheses on a dataset with the help of background information.
—Deductive Learning: This type of AI learning technique starts with the series of rules and
infers new rules that are more efficient in the context of a specific AI algorithm. Explanation-
Based Learning(EBL) and Relevance-0Based Learning(RBL) are examples examples o f
deductive techniques. EBL extracts general rules from examples by “generalizing” the explanation.
RBL focuses on identifying attributes and deductive generalizations from simple example.
36. AI Learning Models: Feedback-Based Classification
Based on the feedback characteristics, AI learning models
can be classified as supervised, unsupervised, semi-
supervised or reinforced.
—Unsupervised Learning: Unsupervised models focus
on learning a pattern in the input data without any external
feedback. Clustering is a classic example of unsupervised
learning models.
37. —Supervised Learning: Supervised learning models use external feedback to
learning functions that map inputs to output observations. In those models the
external environment acts as a “teacher” of the AI algorithms. Linear
regression
—Semi-supervised Learning: Semi-Supervised learning uses a set of curated,
labeled data and tries to infer new labels/attributes on new data sets. Semi-
Supervised learning models are a solid middle ground between supervised and
unsupervised models.
—Reinforcement Learning: Reinforcement learning models use opposite
dynamics such as rewards and punishment to “reinforce” different types of
knowledge. This type of learning technique is becoming really popular in modern AI
solutions.
38. AITools and Learning Models :
One of the core tasks for artificial intelligence is
learning.
Intelligent Agents When solving AI problems, we
create an actor in the environment that can gather
data from its surroundings and influence its
surroundings. This actor is called an intelligent agent.
An intelligent agent: Is autonomous Observes its
surroundings through sensors Acts in its environment
using actuators Directs its activities toward achieving
goals
40. The Role of Python in Artificial
Intelligence
• Python is a high-level programming
language.
• Cross-platform compatible.
• Rapid application development.
• Main advantage is memory efficiency,
as Python can handle large, in-memory
databases.
41. Anaconda in Python
• Anaconda comes with packages, IDEs,
data visualization libraries, and high
performance tools for parallel computing
in one place.
• Anaconda hides configuration problems
and the complexity of maintaining a stack
for data science, machine learning, and
artificial intelligence.
47. Python libraries for AI
NumPy: NumPy is a computing library for Python. As Python
does not come with a built-in array data structure, we have to
use a library to model vectors and matrices efficiently. In data
science, we need these data structures to perform simple
mathematical operations.
SciPy: SciPy is an advanced library containing algorithms that
are used for data science. It is a great complementary library
to NumPy, because it gives you all the advanced algorithms
you need, whether it be a linear algebra algorithm, image
processing tool, or a matrix operation.
48. pandas: pandas provides fast, flexible, and
expressive data structures such as one-dimensional
series and two-dimensional DataFrames. It
efficiently loads, formats, and handles complex
tables of different types.
scikit-learn: scikit-learn is Python's main machine
learning library. It is based on the NumPy and SciPy
libraries. scikit-learn provides you with the
functionality required to perform both
classification and regression, data preprocessing,
as well as supervised and unsupervised learning.
49. NLTK: NLTK is still worth mentioning, because this
library is the main natural language toolkit of Python.
You can perform classification, tokenization,
stemming, tagging, parsing, semantic reasoning, and
many other services
using this library.
TensorFlow: TensorFlow is Google's neural network
library, and it is perfect for implementing deep
learning artificial intelligence. The flexible core of
TensorFlow can be used to solve a vast variety of
numerical computation problems.
Some real-world applications of TensorFlow include
Google voice recognition and object identification.
50. Introduction to the NumPy
Library
• NumPy is a Python library used for working with arrays.
• It also has functions for working in domain of linear
algebra, fourier transform, and matrices.
• NumPy was created in 2005 by Travis Oliphant. It is
an open source project and you can use it freely.
• NumPy stands for Numerical Python.
• The source code for NumPy is located at this github
repository https://github.com/numpy/numpy
51. Operations using Numpy
Using NumPy, a developer can perform the following operations −
● Mathematical and logical operations on arrays.
● Fourier transforms and routines for shape manipulation.
● Operations related to linear algebra. NumPy has in-built
functions for linear algebra and random number
generation.
52. After launching your IPython console, you can simply import NumPy as
follows:
import numpy as np
we can define vectors and matrices
np.array([1,3,5,7])
The output is as follows:
array([1, 3, 5, 7])
We can declare a matrix using the following syntax:
A = np.mat([[1,2],[3,3]])
The output is as follows:
matrix([[1, 2],[3, 3]])
The array method creates an array data structure, while mat creates a matrix.
53. Addition in matrices:
A + A
The output is as follows:
matrix([[2, 4], [6, 6]])
Subtraction in matrices:
A - A
The output is as follows:
matrix([[0, 0],[0, 0]])
Multiplication in matrices
A * A
The output is as follows:
matrix([[ 7, 8], [12, 15]])
Matrix addition and subtraction works cell by cell.
Matrix multiplication works according to linear algebra rules.
54. • Another frequent matrix operation is the determinant
of the matrix.
• The determinant is a number associated with square
matrices. Calculating the determinant using NumPy is
easy:
np.linalg.det( A )
• The output is -3.0000000000000004.
• the determinant can be calculated as 1*3 – 2*3 = -3.
• We can also transpose a matrix, like so:
np.matrix.transpose(A)
• The output is as follows: matrix([[1, 3],[2, 3]])
• When calculating the transpose of a matrix, we flip its
values over its main diagonal.