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Artificial Intelligence ( AI ) Classes Near Me:

Artificial Intelligence ( AI ) Online / Home ( Tutors, Tuition Classes, Teachers, Artificial Intelligence ( AI ) Tuition Classes near me, Artificial Intelligence ( AI ) Tutors near me, Artificial Intelligence ( AI ) Teachers near me, Artificial Intelligence ( AI ) Online Tutors near me, Artificial Intelligence ( AI ) Home Tutors near me, Artificial Intelligence ( AI ) Live Teachers near me ) for Class 9th, Class 10th, Class 11th & Class 12th in Greater Noida, Greater Noida West, Delhi NCR, Gurgaon ( Gurugram ), Ghaziabad, Faridabad, Mumbai, Pune, Hyderabad, Bangalore ( Bengaluru ) and other major locations :

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Artificial Intelligence ( AI ) - Introduction & Scope :

Artificial Intelligence ( AI ) is an upcoming specialisation of the field of computer science CS and has direct applications to real life scenarios , both structured and unstructured. Artificial Intelligence ( AI ) focuses upon developing intelligent machines, machines that can learn and then teach themselves. Subsequently, these machines can act autonomously to process vast amounts of data than humans can, many times faster. Artificial Intelligence ( AI ) is cross - functional with applications across many other scientific and engineering disciplines.

 

Artificial Intelligence ( AI ) has a great potential to change the world, thus, making a conducive environment for the evolution of human race. Artificial Intelligence ( AI ) assists in creating new healthcare solutions, building better medical solutions, bettering farming practices and food supply chains, helping refugees adapt better to new environments, improving educational resources and access, and even cleaning our natural resources including oceans, air, water supply and environment. The potential for human beings to better the world through AI is endless, as long as we know how to use it.

 

Artificial Intelligence ( AI ) is easy to be learnt and has a lot of upcoming employment opportunities that are just going to evolve. Contemporary roles that go along with this specialisation are Data Scientists, Data Architects, ML Engineers, Data Analysts, Game Programmers, Business Intelligence Developers, Software Engineers and AI Research Scientists. More roles and opportunities are going to be developed in the near future that will strongly leverage the tenets of STEM based research. All this will have historical repercussions over the human evolution and the related civics.

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Thus, Wise Turtle Academy attempts to deliver good Quality "Class 12th Artificial Intelligence ( AI ) Online Tuition Classes, Class 12th Artificial Intelligence ( AI ) Home Tuition Classes & Class 12th Artificial Intelligence ( AI ) Online Training near me" in Greater Noida, Noida Extension and Greater Noida West. These classes are successfully delivered through Online Tutoring modes or home tutoring modes or offline tutoring modes to students of different schools and colleges in the various areas of Greater Noida West.

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Wise Turtle Academy - Geographical Coverage & Scope :

Services' Coverage - International ( Overseas & Abroad )

Canada ( British Columbia ( BC ), Canada ( Ca ) & Manitoba ( University of Manitoba ), Canada ( Ca ) ), Australia, Mexico, London, Singapore, Hong-Kong, United States ( Florida, Carolina, New Jersey, Washington ), United Kingdom, Abu Dhabi, Sri Lanka, Bhutan, Nepal, Burma, Malaysia, Bangladesh, Dubai, Africa ( South Africa ), Netherlands, Denmark, Korea, Japan, Asia - Pacific ( APAC ), Americas ( AMER ), Europe, Warsaw, Poland, Russia, France, Germany, Spain, Greece, Belgium, Switzerland and other countries.

 

Services' Coverage - Pan India ( Across India  - Nationally )

Faridabad, Ghaziabad, Delhi ( Kalkaji, Okhla ( Phase - 1, 2, 3 ), Nehru Place, Hauz Khas, South Delhi ( East of Kailash, Kailash Hills, Lajpat Nagar, Okhla Phase 1, Okhla Phase 2, Okhla Phase 3, Srinivaspuri, Kalkaji, Nehru Place, Sant Nagar ), North Delhi, West Delhi, East Delhi ), Gurgaon, Pune, Mumbai, Bangalore, Hyderabad, Andhra Pradesh, Secunderabad, Ahmedabad, Alwar ( Rajasthan ), Jaipur ( Rajasthan ), Bhiwadi ( Rajasthan ), Gurugram, New Delhi, Greater Noida West, Faridabad, Ghaziabad, Gurgaon, Gurugram, Delhi, Noida,

 

Telangana, Bhopal, Gandhinagar, Lucknow, Shimla, Mangalore, Chennai, Noida ( viz., ATS Pristine, Sector 150, Sector 144, Sector 148, Sector 125, Sector 76 Metro Station, Aditya Urban Casa Towers A B C D E F G H I J K L, Sector 78, Mahagun Moderne, Sports City, Hyde Park, Amrapali Princely Estate, Mahagun Mirabella, Civitech Stadia, Sectors 76, 77, 79, 80, 70, 71, 72, 73, 74, 75 ) and other nearby locations.

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Services' Coverage - Greater Noida, Noida Extension, Greater Noida West, Gautam Budh Nagar, Uttar Pradesh & Delhi NCR, India
Core Areas :

Pari Chowk, Omaxe NRI City, Eldeco Greens, Unitech Habitat, Ace Infrastructure, Super Tech Czar Suites Omicron 1/2/3, IFS Society Villas Pari Chowk, SDS NRI Residency Pari Chowk, The Palms Pocket P 7, ATS Pristine, Jaypee Greens ( Sun Court 1, Crescent Court 3 ) Pari Chowk, Metro Line, Sectors Alpha 1, Alpha Commercial Belt, Beta 1, Mu 1, Alpha 2 Main Market, Mu 2, Ansals Golf Links, Eldeco Meadows, Mu 3, Paramount Golf Foreste Studio Apartments, Mu 4, Beta 2, Gama2, Shisham Estate Gama 1 ( Officer's Colony ), Kadamba Estate, Gamma 1 ( Pocket A Officer's Colony, Pocket B , C, D, E, F, G ), Omega 1, Eta 1, Gamma 2, Omega 2, Eta 2, Chi 1, Omega 3,Eta 3, Chi 2, Omega 4, Chi 3, Knowledge Park 3, Delta 5,

 

Eta 4, Xu 1, Phi 1, Xu 2, Phi 2, Xu 3, Sigma 1, Phi 3, Sigma 2, Jalvayu Vihar Society, Sigma 3, Army Welfare Housing Organisation AWHO Twin Towers Societies, CGEWHO, Gamma 2, Sigma 4, Zeta 1, Builder's Area P 1 2 3 4 5 6 7 8 9, Eachhaar, ari Chowk, Cross Streets, Site A, Estates, Pockets, Blocks A B C D E F, LG Chowk, Gama 1, Pi 1, Gamma 1 ( Officers Colony ), Sigma 1, Mu 1, Chi 1, Tau 1, KP 1, Xu 1, Sigma 2, Mu 2, Pi 2, Gamma 2, Ecotech 1, Gama 2, Xu 2, Tau 2, Alpha 1, Site B, Chi 2, Sigma 3, Pi 3, Tau 3, Mu 3, Alpha 2, Beta 1, Xu 3, Chi 3, Beta 2, Xi 1, Sigma 4, Mu 4, Tau 4, Pi 4, Chi 4, Xu 4, Xi 2, KP 2, Phi 1, Xi 3, KP 3, Phi 2, Omicron 1 A, KP 4, Xi 4, Omicron 2, Tau 5, KP 5, Pi 5, Xi 5, Chi 5, Mu 5, Omicron 3, Zeta 1, Zeta 2,

 

Eta 1, Eta2, Delta 1, Ecotech 2, Delta 2, Knowledge Park 1, Delta 3, Knowledge Park 2, Delta 4, Omaxe Connaught Place Mall, Phi 3, NTPC Colony, Rampur Jagir Chowk, Alpha Commercial Belt, UPSIDC Site C, Surajpur, Uttar Pradesh, Site 1, Site 2, UPSIDC Site C, Site 3, Xi 6, Phi 4, Sigma 5, Site 4, Eldeco Greens, Site 5, Army Welfare Housing Organisation AWHO, Site D, Sharda Hospital, Sector 150, ATS Pristine Sector 150 Towers 1 2 3 4 5 6 7 8 9 10, Sector 144, Phi 5, Unitech Horizon, Ecotech 3, Chi 6, Sector 143, Unitech Habitat, Sector 27, Paramount Golf Foreste, Ansals Golf Links, Jaypee Greens ( Kaveri Gate, Narmada Gate - Star Courts 1 2 3 4 5 6 7 8 9 10 ), NTPC Society, Site E, ATS Paradiso, Phi 6,

 

Knowledge Park 4, Swarna Nagari ( Pockets A B C D E F ), Chi 7, NRI City 1 2 3, Silver City 1 2, ACE Platinum, Ecotech 4, The Oasis, The Palms, Builder's Area P 1 2 3 4 5 6 7 8 9, Eachhaar, AVJ Heightss, Migsun Green Mansion, Shivalik Residency, Paramount Villas Society, MSX Alpha Homes, Phi 7, CGEWHO Greater Noida, Site F, Pi 6, Purvanchal Heights, Eldeco Meadows, Knowledge Park 5, Tughalpur Village, Sector 101, Kasna, Yamuna Expressway, Ecotech 5, Sector 31, Wipro Circle Chowk, Sector 32, Rail Colony, Sector 33, Greater Noida Expressway, Sector 34, Rail Society, Sector 35, Toy City, Sector 36, Pi 7, Noida Extension ( Sector 1 Arihant Arden ), Sector 37, Jewar, sector 38, Rail Vihar, sector 39,

 

Jal Vayu Vihar, Sector 40, ATS Paradioso, Ecotech 6, Sector 41, Tilapta Village, Ashiana Orchids, Sector 42, Site G, Tilapta Circle Chowk, Sector 43, Sector 45, Knowledge Park 6, Ecotech 7, Sectors ( 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150 ), Sector 76 Metro Station, Ecotech 8, Site H, Sector 78 ( Aditya Urban Casa Towers A B C D E F G H I, Hyde Park, Civitech Stadia, Amrapali Princely Estate, Mahagun Mirabella, Sports City, Mahagun Moderne ), ​

 

Swarna Nagari ( Pockets A, B, C, D, E, F ), Phi 4, Zeta 2, Zeta 3, HIG Apartment Omicron 1, Chi 4, Xi 4, Zeta 4, Pi 1, Xu 4, Pi 2, Omicron 2, Ashiana Orchids, Pi 3, MSX Alpha Homes, Rail Vihar, Omicron 3, Pi 4, Swarna Nagri, Knowledge Park 1 ( KP 1 ) KP I, Pi 5, Rampur Jagir, Advocate's Colony, The King's Reserve, Purvanchal Heights, Site 1, Ecotech 1, Site 2, Ecotech 2, Site 3, Ecotech 3, Site 4, ATS Paradioso, Ecotech 4, Site A, The Oasis, IFS Villas, Shivalik Residency, Site B, Oasis Venetia Heights, UPSIDC Site C, Uttar Pradesh, Stellar Mi Legacy, Site D, Silver City 1 2 3, Migsun Green Mansion, Site E, DesignArch, Site 5, Eachhaar, Site 6, Makora, Site 7, Knowledge Park 2 ( KP 2 ) KP II, Rail Colony, Jal Vayu Vihar,

 

Knowledge Park 3 ( KP 3 ) KP III, Ashirwaad Apartments, Tughalpur Village, NTPC Colony, The Palms, Jagat Farm, Kulesra, Officer's Colony, NRI City 1 2 3, NRI Colony , LG Chowk, Surajpur, Kasna Village, Cherry County, Alistair Meadows, The Oasis, AVJ Heightss, ACE Platinum, Alpha Homes ), Greater Noida West ( Gaur City I - 1, Gaur City II - 2, Gaur Chowk ), Pari Chowk, LG Chowk, Gamma 1, Gama 1, Gamma 2, Gama 2, Alpha 1, Xu 1, Mu 1, Pi 1, Chi 1, Sigma 1, Surajpur Site 1, Tau 1, KP 1, Omega 1, Delta 1, Blocks A B C D E F G H I J K L M, Ecotech 1, Eachhaar, Alpha 2, Beta 1, Beta 2, Xi 1, Xi 2, Phi 1, Phi 2, Omicron 1, Omicron 2 A, Omicron 3, Zeta 1, Zeta 2, Eta 1, Eta2, Delta 1, Delta 2, Knowledge Park 1,

 

Knowledge Park 2, Knowledge Park 3, Omaxe Connaught Place Mall, Rampur Jagir Chowk, Alpha Commercial Belt, Surajpur, Sharda Hospital, Sector 150, ATS Pristine Sector 150, Alpha 2, Xu 2, Mu 2, Pi 2, Chi 2, Sigma 2, Surajpur Site 2, Tau 2, KP 2, Omega 2, Sector 144, Sector 143, Sector 27, AWHO, CGEWHO, Swarna Nagari ( Pockets A B C D E F G H I J K L ), Tughalpur Village, Kasna, Greater Noida Expressway, Sector 31, Sector 32, Sector 33, NRI City, Blocks, Sectors, Silver City 1 2 3 4 5, Sector 34, Alpha 3, Xu 3, Mu 3, Pi 3, Chi 3, Sigma 3, Surajpur Site 3, Tau 3, KP 3, Omega 3, Delta 3, Sector 35, Rampur, Sector 36, Alpha 4, Xu 4, Mu 4, Pi 4, Chi 4, Sigma 4, Surajpur Site 4, Tau 4, KP 4, Omega 4, Delta 4,  Sector 37,

 

sector 38, sector 39, Sector 76, Ace Platinum, The King's Reserve, Sector 78 ( Aditya Urban Casa Towers A B C D E F G H I, Hyde Park, UPSIDC Site B, Surajpur, Greater Noida, Uttar Pradesh, Block C, Amrapali Princely Estate, Civitech Stadia, Mahagun Mirabella, Mahagun Moderne ), Migsun Mansion Greens, Shivalik, Jaypee Greens ( Narmada Gate Star Court Tower 1 2 3 4 5 6 7 8 9 10 ), Paramount Golf Foreste, Purvanchal Heights, AVJ Heights, MSX Alpha Homes, Rail Vihar, Rail Colony, Jal Vihar, Judge Society, IFS Villas, Ashirvad Apartments, Sector 40, IRDO Colony Apartment, Builder's Area P 1 2 3 4 5 6 7 8 9, Alpha 5, Xu 5, Mu 5, Pi 5, Chi 5, Xi 5, DesignArch, Shivalik, Makora, Tilapta, AVJ Heights, Sigma 5,

 

Surajpur Site 5, Tau 5, KP 5, Omega 5, Delta 5, Sector 41, Sector 42, Noida Extension ( Sector 1 Arihant Arden ), Sector 43, Ace Platina, Ace Platinum, Sector 45, Greater Noida West ( Gaur Chowk, Gaur City 1, Gaur City 2, Saya Zoin ).

Peripheral Areas - Inner :

Migsun Green Mansion, Stellar MI Legacy, Ratan Vihar, Defence Empire I, Tilpata Golchakkar, Devla, Village Tilpatta, UPSIDC Site C, Site F, Site G, Site H, Site I, Site J, Site K, Site L, Site M, Surajpur Industrial Area, Tata Enclave, Anand Ashray Complex, Chorisia Speciosa Estate, NHPC Society, CGEWHO Project, Kendriya Vihar, Nirman Kunj CPWD Society, Unitech Heights, Vrinda City Apartment, Shri Kripa Kunj, Icon Apartments, Adarsh Vihar Society, Ottoman Turkish Baklava, Green Noida Vertical Garden, Purvanchal Royal City Phase 1, Earthcon Casa Grande 2, Sampada Livia, Express Park View 1 2 Apartments, Durva Greens, Chuharpur Market, Lohiya Enclave, Red Building, Lal Building,

 

Omicron 1A Kali Building, Omicron 1B, Omicron 1C, Omicron 1D, Possession Office, Omaxe Orchid Avenue, Omaxe Society, EWS Society, Stellar MI Citihomes, Ropan, Eldeco Residency Greens, Cassia Estate Society, BSF Housing Society, Aichhar, Parsvnath Estate, Swarn Nagri ( Pockets A B C D E F G H I J K L ), Ambey Bharti Apartment, LG Cooperative Housing Society, Jamia Apartment, Khushboo Apartments, Khushboo Sahkari Awas Samiti, Sun Twilight Villas, Jyoti Kiran Society, White House Apartments, Unitech Cascades Apartment, Tower 1, Tower 15, Tower 18, Plumeria Garden Estate, Fairway Apartment Tower D, Nambardar Residence, Ajju Prajapati Town, Anjana Welfare Society,

 

Paradise Dream City, Defence Empire 2, Eden Golf Group, Victory Enclave, Shyam Enclave, Mahamaya Enclave, Royal Paradise, Prithvi Greens, Bhoomi Greens Phase 2, Shiv Enclave, Kartik Nagar, Himalaya Hi Tech City, Ajnara City, Galactic City, Ace City, Ace Divino, ATS Destinaire, Arihant Ambar, Flora Heritage, The Palm Valley, Akshardham Colony, Noor Colony, Vidyapati Nagar, Jalpura, Jamia Nagar Colony, Tusiana Village, Tusyana, Supertech, Amrapali West, Udyog Vihar, Brahmpur Rajraula, Nawada, Parsvnath Privilege, Parsvnath Edens.

Peripheral Areas - Outer :

NTPC Society, Nirman Vihar, Techoma Estate, Cassia Fistula Estate, Rasoolpur Rai, Jaitpur Village, Ekanki Enclave ( Block G ), Vimal Sadan Society, Cassia Sigma, Sigma Group Housing Society, Chorosia Estate, Grand Forte, BSNL Society, Kyampur, Ecotech Extension 1, Migsun Ultimo Sun 3, Omaxe Palm Greens, KKS Homes, Ghodi Bachheda, Ghori Bachhera, MamaPikin Suya House, Ebony Estate, Austonia Estate, ATS Paradiso, Lagerstroemia Estate, Cassia Nodosa Estate, Chakrasia Estate, Himsagar Apartment, Mitra Enclave, Surajpur Site 4, Godrej Golf Links, Ansal Golf Links 1, Surajpur Site 1, Surajpur Site 2, Surajpur Site 3, Surajpur Site 4, Surajpur Site 5,

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Surajpur Site 6, Block A, Block B, Block C, Block D, Block E, Block F, Block H, Block I, Block J, Block K, Block L, Block M, 1st Cross Street, 2nd Cross Street, 3rd Cross Street, 4th Cross Street, 5th Cross Street, 6th Cross Street, 7th Cross Street, 8th Cross Street, 9th Cross Street, 10th Cross Street, Service Road, First Avenue, Second Avenue, Third Avenue, Fourth Avenue, Fifth Avenue,1st Avenue, 2nd Avenue, 3rd Avenue, 4th Avenue, 5th Avenue, 6th Avenue, 7th Avenue, 8th Avenue, 9th Avenue, 10th Avenue,

 

ATS Dolce, Amrapali Grand Apartment, Migsun Wynn, SKA Metro Ville, Cluster ETA 2, 1st Cross Avenue, 2nd Cross Avenue, 3rd Cross Avenue, 4th Cross Avenue, 5th Cross Avenue, 6th Cross Avenue, 7th Cross Avenue, 8th Cross Avenue, 9th Cross Avenue, 10th Cross Avenue, 11th Cross Avenue, Main Road, Tilpata Karanwas, Luharli, Ajayabpur, Eachachhar, Accher, Habibpur, Gujarpur, Jhatta, Gulavali, Malakpur, Judge Society, Amit Nagar, NTPC Anandam Society,  Purvanchal Silver City 2,

 

Unitech Horizon, Alistonia Estate, Sector 34, Gulistanpur Village, Gulmohar Estate, Theta 1, Theta 2, Theta 3, Theta 4, Theta 5, Delhi Police Housing Society, Gaur Atulyam, Eldeco Mystic Greens, Palash Estate, Pocket 4, Khadar Ke Marhiya, Dadha, Sadar Tehsil, GNIDA BHS 16 Housing Scheme, Bironda, Haier Industrial Park, Sakipur, Makora, Tugalpur, NSG Society, Gurjinder Vihar.

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What are the elements of Artificial Intelligence or AI ? 

AI programmes can intelligently respond to queries and draw conclusions about actual facts thanks to knowledge engineering and representation of information. In fields such as scene interpretation, clinical decision support, knowledge discovery ( extracting "interesting" and usable inferences from huge databases ) and others, formal knowledge representations are used. Content - based indexing and retrieval. Knowledge bases are collections of knowledge that are presented in ways that programmes may use.

 

The group of things, connections, ideas, and characteristics that a body of knowledge uses as an ontology. Things like the following must be represented in knowledge bases. First are objects, their attributes, classifications and relationships among them. Conditions, occurrences, states, and time come in second. The third group is the causes and effects. The understanding about knowledge comes in at number four. The latter relate to our knowledge of other people's knowledge.

 

Fifth is focused on default thinking. The latter is all about the beliefs that people have until they are informed otherwise. It will continue to be true even while other facts change. Sixth one covers a wide range of additional topics and subject areas. The following is one of the trickiest KR puzzles. The range of common sense information. The latter is the vast collection of atomic facts that the average person is aware of. Most common sense information in people's context can be expressed sub - symbolically as follows. 

 

A lot of what individuals know but do not correspond to "facts" or "statements" that they can verbally express. The challenging issue with acquiring knowledge for AI applications is knowledge acquisition. By "scraping" the internet, contemporary AI compiles knowledge. The volunteers and experts who published the information gathered the knowledge themselves. The accuracy or dependability of the knowledge is not assured by the "crowd sourced" methodology.

 

Large Language Model LLM knowledge, such that produced by Chat - GPT, is still evolving. It needs to be tuned well to come up to a recognised level and globally established standards. Latter should be with a focus to deliver accurate and certified information. It's a challenge to provide reliable knowledge for these contemporary AI applications.

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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Artificial Intelligence ( AI ) - Class 9th ( Class IX - 9 ) :

UNIT 1: INTRODUCTION TO AI
Excite To identify and appreciate Artificial Intelligence and describe its applications in daily life.
Session: Introduction to AI and setting up the context of the curriculum
Ice Breaker Activity: Dream Smart Home idea
Learners to design a rough layout of floor plan of their dream smart home.
To relate, apply and reflect on the Human - Machine Interactions.
To identify and interact with the three domains of AI: Data, Computer Vision and Natural Language Processing.
Recommended Activity: The AI Game
Learners to participate in three games based on different AI domains.
− Game 1: Rock, Paper and Scissors ( based on data )
− Game 2: Mystery Animal ( based on Natural Language Processing - NLP )
− Game 3: Emoji Scavenger Hunt ( based on Computer Vision - CV )
To undergo an assessment for analysing progress towards acquired AI - Readiness skills.
Recommended Activity:
AI Quiz ( Paper Pen / Online Quiz )
To imagine, examine and reflect on the skills required for futuristic job opportunities.
Recommended Activity: To write a letter
Writing a Letter to one’s future self 
Learners to write a letter to self - keeping the future in context. They will describe what they have learnt so far or what they would like to learn someday
Relate Learners to relate to application of Artificial Intelligence in their daily lives.
Video Session: To watch a video
Introducing the concept of Smart Cities, Smart Schools and Smart Homes
To unleash their imagination towards smart homes and build an interactive story around it.
To relate, apply and reflect on the Human - Machine Interactions.
Recommended Activity: Write an Interactive Story
Learners to draw a floor plan of a Home, School, City and write an interactive story around it using Story Speaker extension in Google docs.
Purpose To understand the impact of Artificial Intelligence on Sustainable Development Goals to develop responsible citizenship.
Session:
Introduction to UN Sustainable Development Goals
Recommended Activity: Go Goals Board Game
Learners to answer questions on Sustainable Development Goals
Possibilities To research and develop awareness of skills required for jobs of the future.
To imagine, examine and reflect on the skills required for the futuristic opportunities.
To develop effective communication and collaborative work skills.
Session: Theme - based research and Case Studies
Learners will listen to various case - studies of inspiring start - ups, companies or communities where AI has been involved in real - life.
Learners will be allotted a theme around which they need to search for present AI trends and have to visualise the future of AI in and around their respective theme.
Recommended Activity: Job Ad Creating activity
Learners to create a job advertisement for a firm describing the nature of job available and the skill - set required for it 10 years down the line. They need to figure out how AI is going to transform the nature of jobs and create the Ad accordingly.
AI Ethics To understand and reflect on the ethical issues around AI.
Video Session: Discussing about AI Ethics
Recommended Activity: Ethics Awareness
Students play the role of major stakeholders and they have to decide what is ethical and what is not for a given scenario.
To gain awareness around AI bias and AI access.
Session: AI Bias and AI Access
Discussing about the possible bias in data collection
Discussing about the implications of AI technology
To let the students analyse the advantages and disadvantages of Artificial Intelligence.
Recommended Activity: Balloon Debate
Students divide in teams of 3 and 2 teams are given same theme. One team goes in affirmation to AI for their section while the other one goes against it.
They have to come up with their points as to why AI is beneficial / harmful for the society.
 

UNIT 2: AI PROJECT CYCLE :
Problem Scoping
Identify the AI Project Cycle framework.
Session: Introduction to AI Project Cycle
Problem Scoping
Data Acquisition
Data Exploration
Modelling
Evaluation
Learn problem scoping and ways to set goals for an AI project.
Activity: Brainstorm around the theme provided and set a goal for the AI project.
Discuss various topics within the given theme and select one.
List down / Draw a mind map of problems related to the selected topic and choose one problem to be the goal for the project.
Identify stakeholders involved in the problem scoped.
Brainstorm on the ethical issues involved around the problem selected.
Activity: To set actions around the goal.
List down the stakeholders involved in the problem.
Search on the current actions taken to solve this problem.
Think around the ethics involved in the goal of your project.
Understand the iterative nature of problem scoping for in the AI project cycle.
Foresee the kind of data required and the kind of analysis to be done.
Activity: Data and Analysis
What are the data features needed?
Where can you get the data?
How frequent do you have to collect the data?
What happens if you don’t have enough data?
What kind of analysis needs to be done?
How will it be validated?
How does the analysis inform the action?
Share what the students have discussed so far.
Presentation: Presenting the goal, actions and data.
Data Acquisition
Identify data requirements and find reliable sources to obtain relevant data.
Activity: Introduction to data and its types.
Students work around the scenarios given to them and think of ways to acquire data.
Data Exploration
To understand the purpose of Data Visualisation
Session: Data Visualisation
Need of visualising data
Ways to visualise data using various types of graphical tools.
Use various types of graphs to visualise acquired data.
Recommended Activity: Let’s use Graphical Tools
To decide what kind of data is required for a given scenario and acquire the same.
To select an appropriate graphical format to represent the data acquired.
Presenting the graph sketched.
Modelling Understand, create and implement the concept of Decision Trees.
Session: Decision Tree
To introduce basic structure of Decision Trees to students.
Recommended Activity: Decision Tree
To design a Decision Tree based on the data given.
Understand and visualise computer’s ability to identify alphabets and handwritings.
Recommended Activity: Pixel It
To create an “AI Model” to classify handwritten letters.
Students develop a model to classify handwritten letters by diving the alphabets into pixels.
Pixels are then joined together to analyse a pattern amongst same alphabets and to differentiate the different ones.
 

UNIT 3: NEURAL NETWORK :
Understand and appreciate the concept of Neural Network through gamification.
Session: Introduction to neural network
Relation between the neural network and nervous system in human body
Describing the function of neural network.
Recommended Activity: Creating a Human Neural Network
Students split in four teams each representing input layer ( X students ), hidden layer 1 ( Y students ), hidden layer 2 ( Z students ) and output layer ( 1 student ) respectively.
Input layer gets data which is passed on to hidden layers after some processing. The output layer finally gets all information and gives meaningful information as output.
 

UNIT 4: INTRODUCTION TO PYTHON :
NOTE: Python should be assessed through Practicals only and should not be assessed with the Theory Exam.
Learn basic programming skills through gamified platforms.
Recommended Activity:
Introduction to programming using Online Gaming portals like Code Combat.
Acquire introductory Python programming skills in a very user - friendly format.
Session:
Introduction to Python language
Introducing python programming and its applications
Practical: Python Basics
Students go through lessons on Python Basics
( Variables, Arithmetic Operators, Expressions, Data Types - integer, float, strings, using print() and input() functions )
Students will try some simple problem solving exercises on Python Compiler.
Practical: Python Lists
Students go through lessons on Python Lists ( Simple operations using list )
Students will try some basic problem solving exercises using lists on Python Compiler.

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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Artificial Intelligence ( AI ) - Class 10th ( Class X - 10 ) :

1. INTRODUCTION TO AI
Foundational concepts of AI
Session: What is Intelligence?
Session: Decision Making.
How do you make decisions?
Make your choices!
Session: what is Artificial Intelligence and what is not?
Basics of AI: Let’s Get Started
Session: Introduction to AI and related terminologies.
Introducing AI, ML & DL.
Introduction to AI Domains ( Data, CV & NLP )
Session: Applications of AI – A look at Real - life AI implementations
Session: AI Ethics
 

2. AI PROJECT CYCLE
Introduction Session: Introduction to AI Project Cycle
Problem Scoping Session: Understanding Problem Scoping & Sustainable Development Goals
Data Acquisition Session: Simplifying Data Acquisition
Data Exploration Session: Visualising Data
Modelling Session: Introduction to modelling
Introduction to Rule Based & Learning Based AI Approaches
Introduction to Supervised Unsupervised & Reinforcement Learning Models
Neural Networks
Evaluation Session: Evaluating the idea!
 

3. ADVANCE PYTHON
( To be assessed through Practicals )
Recap Session: Jupyter Notebook
Session: Introduction to Python
Session: Python Basics
 

4. DATA SCIENCES DS
( To be assessed through Practicals )
Introduction Session: Introduction to Data Science DS
Session: Applications of Data Science DS
Session: Revisiting AI Project Cycle
Concepts of Data Sciences DS
Session: Python for Data Sciences DS
Session: Statistical Learning & Data Visualisation 

K - nearest neighbour model
Activity: Personality Prediction
Session: Understanding K - nearest neighbour model
 

5. COMPUTER VISION
( To be assessed through Practicals )
Introduction Session: Introduction to Computer Vision
Session: Applications of CV
Concepts of Computer Vision
Session & Activity: Understanding CV Concepts
Pixels
How do computers see images?
Image Features
OpenCV Session: Introduction to OpenCV
Hands - on: Image Processing 
Convolution Operator
Session: Understanding Convolution operator
Activity: Convolution Operator
Convolution
Neural Network
Session: Introduction to CNN
Session: Understanding CNN
Kernel
Layers of CNN
Activity: Testing CNN
 

6. NATURAL LANGUAGE PROCESSING NLP / NLU
Introduction Session: Introduction to Natural Language Processing
Session: NLP Applications
Session: Revisiting AI Project Cycle
Chatbots Activity: Introduction to Chatbots
Language Differences
Session: Human Language VS Computer Language
Concepts of Natural Language Processing
Hands - on: Text processing
Data Processing
Bag of Words
TFIDF
NLTK
 

7. EVALUATION

Introduction Session: Introduction to Model Evaluation
Confusion Matrix Session & Activity: Confusion Matrix
Evaluation Score
Calculation
Session: Understanding Accuracy, Precision, Recall & F1 Score
Activity: Practice Evaluation
* NOTE: Unit 3, 4 & 5 should be assessed through Practicals only and should not be assessed with the Theory Exam. 

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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Artificial Intelligence ( AI ) - Class 11th ( Class XI - 11 ) :

LEVEL I:

AI INFORMED ( AI Foundations ) -
Unit 1:
Introduction ( knowledge )
Introduction - AI for everyone
What is AI?
Kids can AI
History of AI
What is Machine Learning
Difference between conventional programming and machine learning
How is Machine learning related to AI?
What is data?
Structured
Unstructured
Examples of unstructured data - text, images
Terminology and Related Concepts Intro to AI
Machine learning
Supervised learning ( examples )
Unsupervised learning ( examples )
Deep learning
Reinforcement learning
Machine Learning Techniques and Training
Neural Networks
What machine learning can and cannot do
More examples of what machine learning can and cannot do
Jobs in AI
Knowledge – Define AI and ML
Comprehension – What are the AI products / applications in society and how are they different from non - AI products / applications ?
Evaluation – What kind of jobs may appear in the future?
 

Unit 2: AI Applications and Methodologies ( Introduction )
( Knowledge )
Present day AI and Applications
Key Fields of Application in AI
Chatbots ( Natural Language Processing, speech )
Alexa, Siri and others
Computer vision
Weather Predictions
Price forecast for commodities
Self - driving cars
Characteristics and types of AI 
Data driven
Autonomous systems
Recommender systems
Knowledge – Where can AI be applied ( like in the field of Computer vision, Speech, Text, etc.), What is deep learning?
Comprehension – How AI will impact our society
Analysis – How should we get ready for the AI age ( future )
Human like
Cognitive Computing ( Perception, Learning, Reasoning ) Cognitive computing
Recommended deep - dive in NLP, CV, etc.*
AI and Society coursera - ai - for - everyone
The Future with AI, and AI in Action 

( Introduction )
Non - technical explanation of deep learning coursera-ai-for-everyone
 

Unit 3: Maths for AI
( Recap )
( Knowledge )
Introduction to matrices ( Recap )
Introduction to set theory ( Recap )
Introduction to data table joins
Simple statistical concepts
Visual representation of data, bar graph, histogram, frequency bins, scatter plots, etc.
With co-ordinates and graphs introduction to dimensionality of data
Simple linear equation
Least square method of regression
Comprehension – Linear Algebra, Statistics, Basics of Graphs and Set theory
Application – Application of Math in AI
Synthesis – Representing data in term of mathematical formula
 

Unit 4: AI Values ( Ethical decision making )
( Values )
AI: Issues, Concerns and Ethical Considerations
Issues and Concerns around AI
AI and Ethical Concerns
AI and Bias
AI: Ethics, Bias, and Trust
Employment and AI 
Knowledge – Ethics, Bias, Impacts of bias on society
Application – Spot issue in data, Make arguments, Apply rules
 

Unit 5: Introduction to story telling
( Skills )
Storytelling: communication across the ages
Learn why storytelling is so powerful and cross - cultural, and what this means for data storytelling
The Need for Storytelling
Story telling with data
By the numbers: How to tell a great story with your data.
Conflict and Resolution
Everyone wants to resolve conflict, and a good data storyteller is there to help!
Storytelling for audience
Your data storytelling depends on the background knowledge of your audience.
Skill – Imagination, mapping the plot into key events increasing memory retention.
Application - Helping in creating blogs, videos, and other content.
Insights from storytelling
Make the audience care about the data
Keep the audience engaged
Create from the end; present from the beginning
Start with an anecdote, end with the data
Build suspense, not surprise

 

LEVEL 2: AI INQUIRED ( AI Apply )
Unit 6: Critical and Creative thinking ( Skills )
Design thinking framework
Right questioning ( 5W and 1H )
Identifying the problem to solve
Ideate
Skill – Understanding the problem and being able to express the same
Creativity – To be able to develop / innovate from design a solution
 

Unit 7: Data Analysis ( Computational thinking )
( Skills )
Types of structured data
Date and time
String
Categorical
Representation of data
Exploring Data Exploring data ( Pattern recognition )
Cases, variables and levels of measurement
Data matrix and frequency table
Graphs and shapes of distributions
Mode, median and mean
Range, interquartile range and box plot*
Variance and standard deviation*
Z-scores*
Example
Practice exercise
Knowledge – Types of structured data, statistical principals – frequency tables, mean, median, mode, range, etc.
Application – Representing data in terms of graphs, statistical models
Synthesis – To be able to represent a simple problem in terms of numbers
 

Unit 8: Regression ( Knowledge )
Correlation and Regression
Crosstabs and scatterplots
Pearson's r
Regression - Finding the line
Regression - Describing Knowledge – Correlations, Regression, and other related terms
Applications – Being able to relate data with regression and correlation. Everyday the line
Regression - How good is the line?
Correlation is not causation
Example contingency table
Example Pearson's r and regression
Readings
Correlation
Regression
Caveats and examples
Practice exercise
Correlation and Regression
Explain the importance of data from above examples
How prediction changes with changing data?
applications of these mathematical concepts.
 

Unit 9: Classification & Clustering ( Knowledge )
What is a classification problem?
Examples
- Simple binary classification
Introduction to binary classification with logistic regression
True positives, true negatives, false positives and false negatives
Where we should care more with examples
Example- false negative of a disease detection can have different implication than false positive, one will be more physical harm and other will be mental
Practice exercise on simple Binary Classification model
Knowledge – What is classification and its types,
what kind of problems may be placed under the category of a classification problem
Applications – Where to apply classification principals 
Analysis – Impact of the application of incorrect algorithms on society
What is a clustering problem?
Why is it unsupervised?
Examples
Practice exercise on simple Clustering model
Knowledge – Clustering problems and its application, why is it called clustering
Application – Application of clustering problem using standard models
 

Unit 10: AI Values ( Bias awareness )
AI working for good
Principles for ethical AI
Knowledge – What is ethics, Impact of ethics on society,
( Values )

Types of bias ( personal, cultural, societal )
How bias influences AI based decisions
How data driven decisions can be de - biased
Hands on exercise to Detect the Bias ( Intro to AI )
the impact of bias on AI functioning
Evaluation – Biases in data, how to de - bias or neutralize the biased data
Application – Finding bias in acquired data - set
NOTE: UNITS 2, 5, 6, 7 & 10 should be assessed through Practicals only and should not be assessed with the Theory Exam. 

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CAREER OPPORTUNITIES :
Data Scientist
Data Architect
ML Engineer
Data Analyst
Game Programmer
Business Intelligence Developer
Software Engineer – AI
AI Research Scientist

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Following is the general CBSE / NCERT prescribed outline of the syllabus for subject Artificial Intelligence ( AI ) - Class 12th ( Class XII - 12 ) :

AI Innovate - ( Level 3 )
Unit 1: Capstone Project
Understanding the problem
Decomposing the problem through DT framework
Analytic Approach
Data Requirements
Data Collection
Modelling approach
How to validate model quality
By test - train split
Introduce concept of cross validation
Metrics of model quality by simple Maths and examples from small data - sets – scaled up to capstone project ( Apply )
RMSE- Root Mean Squared Error
MSE – Mean Squared Error
MAPE – Mean Absolute Percent Error
Introduction to commonly used algorithms and the science behind them
Showcase through a compelling story
 

Unit 2: Model lifecycle ( Knowledge )
Different aspects of Model
Train, test, validate,
What are hyper parameters
Commonly used platforms to build and run models ( Introduction )
Recommended tools
Links to different platforms
Watson
Lifecycle of an AI model
Build
Deploy
Retrain

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AI Innovate - ( Level 3 )
Unit 3: Storytelling through data
( Critical and Creative thinking Skills )
The Need for Storytelling
Information processing and recalling stories
Why is storytelling important?
Structure that story!
How to create stories?
Begin with a pen - paper approach
Dig deeper to identify the sole purpose of your story
Use powerful headings
Design a Road - Map
Conclude with brevity
Ethics of storytelling
Types of Data and Suitable Charts
Text [ Wordclouds ]
Mixed [ Facet Grids ]
Numeric [ Line Charts / Bar Charts ]
Stocks [ Candlestick Charts ]
Geographic [ Maps ]
Stories During the Steps of Predictive Modeling
Data Exploration
Feature Visualizing
Model Creation
Model Comparisons
Best Practices of Storytelling
Reference Material / Online Resources :
Analytics Vidhya
Udemy:
Coursera:
Coursera:
Student Project Work
( Practical )
Student capstone project development
Students to form teams and work on developing an AI based project
Resources like the AI Project Guide and AI Project Log Book to be used

 

CAREER OPPORTUNITIES :
Data Scientist
Data Architect
ML Engineer
Data Analyst
Game Programmer
Business Intelligence Developer
Software Engineer – AI
AI Research Scientist

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The intelligence of computers or software is known as artificial intelligence ( AI ). It is in conflict with either human or animal intellect. It is also the area of CS computer science where intelligent devices are created and studied. The term "AI" can also refer to the actual devices. The usage of AI technology is widespread in business, government and academia. Here are some prominent uses. The first category includes sophisticated web search tools like Bing, Yahoo, Google Search, etc.

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