AI Notes

Introduction to AI

Artificial intelligence (AI) is the ability of machines to perform tasks that typically require human-like intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Yoshua Bengio: AI is the ability of machines to perform tasks that typically require human-like intelligence.
John McCarthy coined the term "Artificial Intelligence" in his proposal for the 1956 Dartmouth Conference.
AI is the science and engineering of making intelligent machines.
AI is the simulation of human intelligence in machines designed to think, make decisions, predict the future, learn, and improve.
AI systems can perform tasks needing human intelligence.
AI builds robots or virtual assistants that mimic human behaviour and make decisions.
A machine is artificially intelligent when it:
- Thinks and learns like humans
- Comprehends language and images
- Mimics human intelligence
- Detects patterns and solves real-world problems
- Improves on its own
- Predicts and takes decisions on its own

Brief History of AI

Exploration of the human brain dates back thousands of years.
Scientists believed advanced electronics could simulate brain processes artificially.
Modern AI began around the 1940s.
1940s-1950s:
- Pioneers like Alan Turing, John McCarthy, and Marvin Minsky laid foundations for AI research.
- They explored concepts such as machine learning, logic, and reasoning.
1960s-1970s:
- Progress slowed due to limited computing power and funding (the 'AI winter').
- Researchers worked on expert systems to simulate human expert knowledge and reasoning.
1980s-1990s:
- AI research revived due to advances in machine learning and neural networks.
- Applications like speech recognition and computer vision emerged.
2000s-2010s:
- Big data and cloud computing enabled new AI forms, like natural language processing and recommendation systems.
- Companies like Google, Facebook, and Amazon invested heavily in AI research and development.
- Advancements occurred in robotics and autonomous vehicles.
2020s and beyond:
- AI continues to evolve and impact various industries, including healthcare, finance, entertainment, and education.

AI Timeline and Milestones

1950: Alan Turing proposed the Turing test to check a machine's ability to exhibit intelligent behavior similar to human intelligence.
1956: John McCarthy coined the term "Artificial Intelligence" at Dartmouth.
1966: Joseph Weizenbaum created ELIZA, the first chatbot, which could trick users into thinking they were talking to a real human being.
1972: The first intelligent humanoid robot, WABOT-1, was built in Japan.
1980: Expert Systems were developed.
1997: IBM's Deep Blue became the first AI-based program to beat the world chess champion, Gary Kasparov.
2002: AI entered homes with Roomba, a vacuum cleaner.
2006: Companies like Facebook, Twitter, and Netflix started using AI.
2009: Google launched its first self-driving or autonomous car, Waymo.
2011: IBM Watson, using natural language processing, won the quiz show Jeopardy!.
2012: Personal digital assistants, like Siri and Google Now, were introduced.
2014:
- Eugene Goostman, a chatbot, was the first AI device to pass the Turing test.
- Pepper, one of the first social humanoid robots that talks and mimics human emotions, was introduced.
2016:
- Amazon Echo, a voice-controlled speaker using AI to power Alexa, was introduced.
- AlphaGo, Google's DeepMind AI, defeated one of the world's top Go players, Lee Sedol.
2018: Google Duplex, a conversational AI, was announced by CEO Sundar Pichai.

AI Around Us

AI is an inseparable part of our lives.
Popular AI-powered applications:
- Search engines (Google): Use AI to display results, auto-correct sentences, and show personalized ads.
- Digital voice assistants (Siri, Google Assistant): Use AI to answer queries and carry out tasks.
- Chatbots: Provide customer support 24/7 on sites like Amazon and Flipkart.
- Navigation apps (Google Maps): Use AI to provide voice-guided instructions and suggest the best routes.
- Games (FIFA): Enhanced graphics and difficulty levels are powered by AI.
- Social media platforms: Use AI to recommend content, provide selfie filters, and use face recognition.
- Health apps: Use AI to monitor physical and mental health and provide personalized plans.
- Social robots (Sophia, Pepper): Use AI to interact with humans.
- Recommendation systems: E-commerce retailers like Amazon track preferences to suggest purchases; OTT platforms like Netflix suggest content based on viewing history.
- Biometric security systems: Face locks are used in phones and laptops for security.
- Language translators (Google Translate): Translate text and speech and identify and read text from images.

Domains of AI

AI coordinates with various domains to replicate human intelligence:
- Data
- Natural Language Processing (NLP)
- Computer Vision (CV)

Data

Data is the core of AI in audio, video, or text form.
AI algorithms use high-quality, reliable data as input for predictions.
AI often uses large amounts of data, termed Big Data.
Big Data refers to the storage and processing of massive data sets.
The larger the data, the more AI systems can learn and produce accurate results.
Big data is the fuel of AI.

Statistical Data

Statistical Data refers to the use of statistical techniques to analyze, interpret, and draw insights from numerical/tabular data.
Objective: Collect large amounts of data, analyze, interpret, and derive meaning for decision-making.

Computer Vision

Computer Vision (CV) enables computers to "see" and derive meaningful information from digital content (photos, videos).
Computers analyze information and make decisions like humans.
To a computer, a picture is a series of pixels (dots) or numerical values representing colors.
Main objective: Teach machines to collect information from pixels and make sense of it.
Process: Image acquiring, screening, analyzing, identifying, and extracting information.
Comparison of Human Vision vs. Computer Vision:
- Human Vision:
  - Eyes sense images, and the brain identifies them through learning and experience.
- Computer Vision:
  - AI perceives the image with technology.
  - Computer Vision and AI algorithms identify and classify elements in the image to recognize it.
Common Computer Vision Applications:
- Self-driving automobiles: Detect 360-degree movements of pedestrians, cyclists, vehicles, etc.
- Facial recognition: Identifies people from digital images or videos, used for security and investigation.

Natural Language Processing

Natural Language Processing (NLP) enables computers to understand and interpret human language and text (natural language).
Goal: Take verbal or written input, process it, and understand it.
Common NLP applications:
- Email filters/spam filters: Separate unwanted messages (spam) from genuine emails.
- Virtual assistants and chatbots: Use natural language to give instructions and respond naturally to human language input.

Interrelation of Domains

AI domains are interrelated and often used together in AI applications as followed:
- An AI model perceives an image using Computer Vision.
- Computer Vision and AI algorithms classify elements (characters, objects, background) using data.
- Natural Language Processing interprets and describes the image.

AI Project Cycle

The AI project cycle outlines the steps necessary for creating an AI project.
It helps to understand problems and develop solutions.
When building an AI model, define the problem, set goals, collect data, find patterns, and test performance.
The AI project cycle is a structured framework with different stages for creating an AI system.
It is a systematic and step-by-step process for developing and implementing artificial intelligence projects. Each stage involves careful planning and execution to design and deploy AI solutions.
The stages include:
- Problem Scoping
- Data Acquisition
- Data Exploration
- Modeling
- Evaluation
- Deployment

AI Project Cycle - Defined

Problem scoping: Understand and define the problem; set clear goals and outline objectives.
- Includes outlining the issues, defining them explicitly, identifying causes, and developing a plan to fix them.
Data acquisition: Collect relevant data from reliable and authentic sources; ensure sufficient quantity.
Data exploration: Explore and analyze collected data to interpret patterns, trends, and relationships.
- Use visual representations like graphs, databases, flowcharts, and maps.
Modelling: Select an appropriate AI model that can learn from data and make predictions.
Evaluation: Test the selected AI model and compare results with expected outcomes to evaluate accuracy and reliability.
Deployment: Deploy the AI model in the real world once evaluation is complete and generates accurate results.

Scenario: Pest Infestation Damages Crops

Farmers in India face pink bollworm infestations in cotton crops.
The insects are hard to detected because they are not visible to the naked eye.
Advanced tools and techniques are needed.

Applying AI Project Cycle to Pest Infestation Problem

Problem scoping: Identify and define the problem as pink bollworm infestations in cotton crops.
- Include scope, objectives, and desired outcomes.
- Addressing the problem helps protect crops and improve yield.
Data acquisition: Collect data related to infestations, cotton crops, weather conditions, soil quality, farm size, pesticide usage, etc.. Collected from sources like:
- agricultural research institutes, government agencies and local farmers.
Data cleaning: Standardize formats and remove duplicates to ensure accuracy and reliability, fill in missing data.
Data exploration: Identify patterns and trends related to pest infestations, pesticide usage, and crop yields.
Modelling:
- Develop an AI-enabled app for farmers to take pictures of pests.
- AI determines if the image is valid and recommends actions based on pest count and entomologist guidelines.
- Utilizes computer vision algorithms trained on pest image datasets to identify and classify pests.
Evaluation: Compare app results to manual identification methods to assess accuracy and efficiency.
- Test the app by comparing its pest identification results with those of experienced entomologists.
Deployment: Deploy the app to farmers' mobile phones.
- Farmers can capture images of pests and receive recommendations for pest management.

AI Project Cycle Mapping

AI project cycle mapping aligns the steps of an AI project with the standardized phases of the AI project cycle.
Key stages and activities are identified for successful development and deployment.
Purpose: Provide a clear roadmap for project teams and structured approach with developing and implementing AI solutions.

AI Project Cycle Mapping Template (for Pink Bollworm Scenario)

Problem Scoping: It is hard to detect the insects visually, and get rid of them
Data Acquisition: Data collection from farmers and diseased cotton plants
Data Exploration: Data analysis and interacting with the data to understand it
Modelling: Creating AI model that meets requirements to solve the problem
Evaluation: Testing and improving the AI model before it is put to use
Deployment: Implementing the AI model in a mobile application to assist farmers

Why We Need the AI Project Cycle

Provides a structured framework for developing and deploying AI projects.
Ensures all aspects are considered to build effective, reliable, and accurate AI systems.
Benefits:
- Efficiency: Provides a structured framework for development that optimizes processes, simplifies resources, and ensures the final AI solution meets its objectives effectively.
- Modularity: Breaks the project into stages, making it easier to manage and maintain.
  - Problems can be fixed directly, and specific parts can be updated without starting over.

Problem Scoping and Setting Goals

Problem scoping involves choosing a problem to address using AI.
Includes:
- identifying the problem's scope, objectives, and constraints.
- establishing a clear understanding of what needs to be addressed.

SDGs

POVERTY
ZERO HUNGER
GOOD HEALTH AND WELL-BEING
QUALITY EDUCATION
GENDER EQUALITY
CLEAN WATER AND SANITATION
AFFORDABLE AND CLEAN ENERGY
DECENT WORK AND ECONOMIC GROWTH
INDUSTRY, INNOVATION AND INFRASTRUCTURE
REDUCED INEQUALITIES
SUSTAINABLE CITES AND COMMUNITIES
RESPONSIBLE CONSUMPTION AND PRODUCTION
CLIMATE ACTION
LIFE BELOW WATER
LIFE ON LAND
PEACE, JUSTICE AND STRONG INSTITUTIONS
PARTNERSHIPS FOR THE GOALS

More Theme Examples

Smart Cities: Energy efficiency, traffic management, waste management, etc.
Social Media: Sentiment analysis, fake news detection, content management, etc.
Environmental Sustainability: Biodiversity conservation, climate action, pollution control, etc.
Digital Literacy: Online learning platforms, digital awareness, e-books, etc.
Health: Medicinal aid, mobile medications, spreading of diseases, etc.
Entertainment: Media, virtual gaming, interactive advertisements, promotions, etc.
Agriculture: Pest issues, yield rates, sowing and harvesting patterns, etc.
Highway: Land acquisition, land quality, deforestation, road construction, etc.
Vaccination Drive: Vaccine awareness, limited vaccine supply, setting up vaccination centres, training healthcare workers, etc.
Hostel: Limited space and facilities, surveillance systems, maintenance and cleanliness, rent collection, responding to complaints, coordinating maintenance activities, etc.
Smart School Attendance System: Connectivity problems, hardware malfunctions, software bugs, user adoption and resistance, cost and maintenance, inaccurate attendance records, etc.

4Ws Problem Canvas

Aids in problem scoping by identifying factors affecting the problem and defining the project goal.
Provides a structured framework for analyzing and understanding a problem.
Four Crucial Parameters: Who? What? Where? Why?

Who?

Identifies people directly or indirectly affected by the problem (stakeholders).
Stakeholders are involved in the problem and will benefit from the solution.

What?

Determines if the problem really exists.
Gather evidence, like observations, newspapers, social media posts, reports, etc.

Where?

Identifies the context or location of the problem.
Physical locations (cities, specific areas), online platforms, educational institutes, public spaces.
Global or local impact.

Why?

Why the problem is worth solving and how the solution benefits stakeholders and society.
Includes better quality of life, increased efficiency, cost savings, enhanced safety, long-term sustainability, or other positive outcomes.
Thus, the 4Ws canvas helps to define the problem, identify the stakeholders, specify the context, and highlight the importance of solving the problem/.

Problem Statement Template

Summarizes key points from the 4Ws problem canvas into a single template.
Simplifies understanding and remembering important aspects of the problem, makes it simple to understand and remember the important aspects of the problem.
Template structure:
- Stakeholder(s) [Who] has/have a problem [issue, problem, need] that [context, situation] when/while [Where].
- An ideal solution [benefit of solution for them][Why].

Implementing the 4Ws Canvas and Problem Statement Template

Scenario: Air Pollution in Gopalapuram

Gopalapuram is known for its clean and green environment.
Increased air pollution due to industrialization, construction, and vehicular traffic.
Health authorities reported a rise in respiratory problems.
Environmentalists observed a decrease in insect and bird populations.

4Ws Canvas for Air Pollution Scenario

Who?
- Stakeholders: Residents, local government, environmentalists, healthcare authorities, animals, birds, and industrialists.
- Details:
  - Residents' health is affected.
  - Local government implements environmental policies.
  - Environmentalists work for biodiversity preservation.
  - Health authorities provide medical care.
  - Industrialists wish to maintain a sustainable environment.
What?
- Problem: Increased air pollution from industrial emissions and vehicles.
- Consequences: Respiratory problems, health issues, reduced insect and bird populations.
How to prove?
*Local authorities have recorded an increase in
air pollution.
*Health authorities have reported a rise in
respiratory problems and other negative health
effects. Environmentalists have observed a
decrease in insect and bird populations.
Where?
- Context: Impact of pollution felt in daily lives.
- Location: Residential areas, public places, workplaces, green spaces, schools, natural habitats.
Why?
- Value: Better air quality reduces health risks for residents and reduces healthcare burdens.
  This will also help industrialists and authorities
  ensure sustainable development. Environmentalists can be assured of the
  preservation of biodiversity and green spaces
- Improvement: Cleaner living environment; sustainable and environmentally conscious city with enhanced ecological balance.

Problem Statement Template for Air Pollution Scenario

Stakeholders: Residents of Gopalapuram, local government authorities, environmentalists, healthcare authorities, and industrialists.
Problem: Due to an increase in air pollution, there is an increase in health problems among the citizens and disturbance in the delicate balance of the ecosystem.
Where:In residential areas, public places, workplaces, and natural habitats
Solution: Lead to a cleaner and healthier living environment for citizens and contribute to building a sustainable and environmentally conscious city.

Data Acquisition

Data acquisition is the process of acquiring or collecting accurate and reliable data from relevant sources.

What is Data?

Data is a collection of facts or statistics collected for reference or analysis.
Data processed into meaningful information using analysis techniques and algorithms.
Data can be text, video, images, audio, etc.
Data is the fuel for AI algorithms to train them.
AI algorithms learns and work based on patterns and insights derived from data.

Types of Data

During AI Project developement: *Training data
- Testing testing

Training Data

Training data:
- Initial dataset used to train an AI model.
- Helps the AI model learn and identify patterns or perform tasks.
- Data must be aligned with the problem statement and be sufficient, relevant, accurate, and wide-ranging.

Testing Data

Testing data:
- Used to evaluate the performance of the AI model.
- Data the AI algorithm has not seen before.
- Allows checking the accuracy of the AI model.
- Should represent information the AI model will encounter in real-world situations.
Process:
$Training Data \implies AI Model \implies Trained AI Model$
$Testing Data + Trained AI Model \implies Prediction/Decision$

Cricket Match Result Prediction Example:

Predicting the result of a cricket match between Team A and Team B based on pitch conditions.
Training data includes pitch conditions for Team A and Team B in previous matches and match outcomes.
The AI model learns about the relationship between the pitch conditions and the match results during training.
Testing data consists of the pitch conditions for upcoming matches.
The AI model then uses this testing data to predict the most likely match outcome.

Data Features

Data features describe the type of information collected in response to the problem statement.
To determine needed data, visualize factors influencing the problem statement.
Parameters affecting the problem statement directly or indirectly.

Cricket Match Example data features:

Historical pitch conditions, scores, and match outcomes between Team A and Team B.
Pitch conditions of the upcoming matches.

Ways of Data Acquisition

Surveys
Web scraping
Sensors
Cameras
Observations
APIs

Survey Methods

Gathering specific information from a group of people by asking them questions.
Enables collecting valuable data quickly and efficiently.
Conducted on paper, through face-to-face/telephone interviews, or online forms.
Example: population census surveys.

Web Scraping

Collecting information from websites in an automated manner.
Automated web scraping tools, BeautifulSoup or Scrapy, are used to help speed up the process

Sensors

Detect and measure environmental conditions (temperature, pressure, light, sound, motion).
Convert physical parameters into electrical signals or digital data that can be processed and analysed by AI systems.
Integrated with IoT devices; e.g., sensors in smart homes or autonomous cars.

Cameras for Data Acquisition

Capturing visual information in the form of images and videos.
Play an important role in the Computer Vision domain of AI.

Observations

It involves gathering data manually through direct observations of real- world events as they happen
Observing reallife data as it happens manually

APIs

Application Programming Interfaces (APIs) are programs used by developers to acquire data from other programs, services, or databases to extract relevant data required for the AI project.
Access data from other Programs automatically through API data links

Important Factors for Data Collection

Data forms the basis of the AI project; hence, data has to be authentic, reliable, and accurate.
*The internet can be used to acquire data for your project from some random websites,
but such data might not be authentic as its accuracy cannot be proved. Hence, it is
necessary to find a reliable source of data.
The methods of obtaining data should also be authentic and ethical, which means
the data should be collected in a fair and lawful manner that respects the rights of
others. This will help avoid conflicts or negative consequences in the future.
Data should be open-sourced and not somebody's property. Also, accessing private
data without permission is an offence.
One of the most reliable and authentic sources of information is the open source websites hosted by the government. These government portals have information presented in a suitable format that can be downloaded and used. Some of the open source government portals are:
- gov.in
- india.gov.in.

System Maps

System maps are visual diagrams that help to see and understand the different parts or elements of the AI project.
They show how all the elements are connected or related to each other.
Used to understand the system's boundaries and how it interacts with elements in its surroundings.

Parts of a System Map

Elements: Main parts of the system map that work together to achieve a specific purpose.
Relationships or interconnections: Reflect the relationship between the different elements of the system.
Feedback loops: Chains of cause and effect where the output of the system influences its behaviour or inputs.

Types of Feedback loops

Feedback loops can be positive or negative, depending on whether they increase or decrease the effects within the system.

Global Warming Example:

Global warming refers to the long-term increase in Earth's temperature due to human activities, mainly the emission of greenhouse gases into the atmosphere.
Primary elements: Human activities, Greenhouse Gases, and Carbon Dioxide (CO2) Emissions.

System Map Explanation:

Elements are placed in circles and show cause-and-effect relationships with arrows.
Arrowheads depict the direction of the effect, and the sign (+ or -) shows their relationship.
Positive (+) Sign: Direct relationship. If X increases, Y also increases, and vice versa.
Negative (-) Sign: Inverse relationship. If X increases, Y will decrease, and vice versa.

Examples:

Human Activities (+ Loop): Deforestation and burning of fossil fuels emit greenhouse gases, causing an increase in the Earth's average temperature.
Trees (- Loop):
Plants play a crucial role in the global Carbon cycle through a process called photosynthesis. During photosynthesis,
plants absorb Carbon Dioxide (CO2) from the atmosphere and convert it into organic matter while releasing oxygen; thus, removing CO₂ from the atmosphere.
seagrasses can absorb and hold carbon up to 35 times faster than tropical rainforests, accounting for 10-18% of total ocean carbon storage contributing to the prevention of global warming.

Data Exploration

Data exploration is the process of analysing data to discover patterns and gain insights.
Different data visualisation methods are used, like bar chart, line chart, histograms, etc.

Need for Visualizing Data

Simplifies complex data; makes it easier to comprehend.
Helps gain a deeper understanding of trends, relationships, and patterns within the data.
Uncovers hidden relationships or anomalies.
Helps in selecting models for the subsequent AI Project Cycle stage.
Makes it easy to communicate insights, even to non-technical people.

Data Visualization Tools

Manual or automated tools selected based on the data and complexity.
Tableau
Microsoft Power BI
QlikView
Looker Studio
Excel
Plotly
Matplotlib
Highcharts

Tableau

A popular data visualization tool.
User-friendly and interactive tool that
supports large datasets. It provides options
to collaborate with others and securely share
data from a variety of sources.

Microsoft Power BI

It is a cloud-based
business intelligence tool that provides
options to access data from multiple sources
into a single dataset, make interactive
dashboards, evaluate data, create reports, and share data with others.

QlikView

It is a data visualization tool that shows the relationship between each piece
of data that is present in a dataset. Data is held in memory for multiple users; hence, it
gives a super-fast user experience. The relationship between data is not shown by arrows
or lines but by colours.

Looker Studio

It is a free and powerful web-based tool for creating interactive charts,
dashboards, and reports. It offers easy tools to automatically combine data from a wide
range of sources, analyse data, and enable you to collaborate with others in real time.

Excel

 *It offers data visualization.

through graphs, charts, as well
as templates. Excel is a manual
visualisation tool that offers a range
of charts, like column charts, bar
charts, pie charts, line charts, area
charts, scatter charts, etc. It lacks
advanced features and cannot be
used for large amount of data, but
it offers a simple and familiar user
interface.

Plotly

 *It is an open-source module of Python that uses Java script and supports.

interactive web-based 2D and 3D graphs, like line charts, 3D scatter plots, bar charts, area
plots, etc. It is visually attractive and allows customisation of graphs, which makes them
more meaningful and understandable.

Matplotlib

 *Matplotlib is a Python library used for creating static, animated, and

interactive visualisations in Python. It can create high quality and customisable plots.

Highcharts

It is a JavaScript library and a free tool that supports touch-screen-based
platforms, like Android and iOS. It is dynamic and customisable, includes a range of
chart types, and supports integration with web applications.

Ways to Visualize Data with examples.

Bar char: A bar chart is a chart that presents categorical or grouped data with rectangular
bars, where the height or length of the bars is proportional to the values that
they represent.A bar chart is used to show comparison between groups of categories of data
Line chart: A line chart is a chart that is created by plotting a series of points that are connected with the help of a line. It is used to track changes in values over a .period of. time
A line chart is suitable for displaying quantitative values over a specified time
interval to identify trends in the data. The above chart shows the change in the bear population in a forest every year over a number of years. Other examples population over a number temperature and years. are variations in the
Pie Chart: A pie chart represents data in a circular form, divided into sectors. Proportional to the quantity it represents.It helps to understand the contribution of individual components or
categories to the whole.
A pie chart is suitable for displaying data that can be divided into different
categories. It is commonly used to show the proportional or percentage
distribution of different categories within a whole .
Area Chart: An area chart is a graph that combines a line chart and a bar chart to show changes in quantities over time. Similar to a line graph, the data points are
plotted and connected by line segments, but the area below the line is coloured
or shaded.An area chart is suitable for comparing the overall size or distribution of
different groups of data.In the above graph, each individual colored area
represents. the population size of a specific species of animal over the years
Bubble Map: A bubble map uses bubbles of different sizes to represent data points or values on a map. Each bubble represents a specific location or region, and the size of
the bubble is proportional to a particular data value, such as population, sales, etc. The larger the bubble, the higher the value.
These maps help visualise information, like population, sales, or anyother numerical value based on different locations

Modelling

Al modeling refers to the development of a program or algorithm that can be used to draw conclusions or generate predictions based on the available data.
At this stage, an appropriate data model is selected and trained with the data that has been acquired and explored in order to achieve the goal of the AI project.

AI, ML, and DL Definitions

Artificial intelligence: AI refers to the field of computer science that creates machines capable of performing tasks that require human-like intelligence. Al-based systems use algorithms and data to process information and produce the required output. Al machines are capable of learning on their own without human intervention.
Machine learning: Machine learning enables machines to learn on their own and improve with time through experience.
Deep learning: Deep learning enables machines to learn from and perform tasks on large
amount of data or big data. Deep learning is based on neural networks with multiple layers that mimic the
working of the human brain.

Hierarchy of AI, ML, and DL

AI is a broad term that includes both machine learning and deep learning.
Machine learning is a subcategory of AI, and deep learning is a subset of machine learning as it includes multiple machine learning models.
Deep learning is the most advanced form of AI.
Ability of machine to imitate human intelligence
Algorithms to incorporate intelligence into machine by automatically learning from data
Algorithms that mimic human brain to incorporate intelligence into machine

Basic Difference Between AI, ML, and DL

 *The aim of Al is to mimic.

human intelligence to create
intelligent machines and
programs
*The aim of machine
learning is to create
machines that can learn on
their own using data and
improve over time without
being programmed for it.
*The aim of DL is to build neural
networks to mimic working
of the human brain and use
complex algorithms and large
volumes of data to enable an AI
model to learn.
*It is a broad field of computer It is a subset of AI that It is a subset of AI and ML that
science that simulates
intelligent behaviour.
*The machine learns on its own from the data.
al is achieved through rule-based technique It may require less training time.
In rule-based approach, learning is static. It does notadapt to changes.
involves algorithms that
learn patterns from data focuses on training deep neural
networks.
*he machine follows the predefined rules defined by the developer.

data to be grouped The relationships or patterns in the data are not defined by the developer, and the AI model defines its own set of rules The machine communicates the trends it observed in the training data.
Data represented graphically. graphically Data in binary numbers

Machine Learning Models

Rule-based Approach
Learning-Based Approach

Rule Based Approach

The developer feeds data along with some rules to the model. The rules are in the form: "If this happens, then do this".
A rule-based approach refers to Al modeling where the relationships or patterns in the data are defined by the developer. The machine follows the rules defined by the developer and produces the required output.
*A drawback/feature for this approach is that the learning is static. The machine once trained, does not take into consideration any changes made in the original training dataset.
Consider the dataset that defines some conditions on the basis of which we can decide if a child can go out to play football or not. The parameters are:
- Outlook
- Temperature
- Humidity
- and Wind
  *Outlook *Temperature *Humidity *Windy *Play Football
Decision tree: A decision tree is an example of a rule-based approach. It is a graphical
representation used for making decisions based on given conditions it is easy to understand and interpret.
* provides a clear and structured way to determine