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Artificial Intelligence ( AI ) Online/Home ( Tutors, Tuition Classes, Teachers, Artificial Intelligence ( AI ) Tuition Classes, Artificial Intelligence ( AI ) Tutors, Artificial Intelligence ( AI ) Teachers, Artificial Intelligence ( AI ) Online Tutors, Artificial Intelligence ( AI ) Home Tutors, 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 :


Artificial Intelligence ( AI ) - Introduction & Scope :

Artificial Intelligence ( AI ) is an upcoming specialisation of the field of computer science 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 and is applied across many other disciplines. Artificial Intelligence ( AI ) has a great potential to change the world and make it far better for evolution of human race.


Artificial Intelligence ( AI ) assist 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, and water supply. 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 have 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.

Thus, Wise Turtle Academy attempts to deliver good Quality "Class 12th Artificial Intelligence ( AI ) Online Tuition Classes" in Greater Noida. These classes are successfully delivered through Online Tutoring mode to the students of all schools and colleges in the various areas of Greater Noida. The prominent areas of Greater Noida covering "CBSE Class 11th Artificial Intelligence ( AI ) Online Tuition Classes" & Online Tutoring services are Pari Chowk, LG Chowk, Gamma 1, Gamma 2, Alpha 1, Alpha 2, Beta 1, Beta 2, Xi 1, Xi 2, Phi 1, Phi 2, Omicron 1, Omicron 2, 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, Sector 144, Sector 143, Sector 27, Swarna Nagari, Tughalpur Village, Kasna, Greater Noida Expressway, Sector 31, Sector 32, Sector 33, Sector 34, Sector 35, Sector 36, Sector 37, sector 38, sector 39, Sector 40, Sector 41, Sector 42, Sector 43, Sector 45, etc..


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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 ) :

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.
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 afirm 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.
Problem Scoping
Identify the AI Project Cycle framework.
Session: Introduction to AI Project Cycle
Problem Scoping
Data Acquisition
Data Exploration
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.
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.
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.
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.

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

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
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!
(To be assessed through Practicals)
Recap Session: Jupyter Notebook
Session: Introduction to Python
Session: Python Basics
(To be assessed through Practicals)
Introduction Session: Introduction to Data Science
Session: Applications of Data Science
Session: Revisiting AI Project Cycle
Concepts of Data Sciences
Session: Python for Data Sciences
Session: Statistical Learning & Data Visualisation 

K-nearest neighbour model
Activity: Personality Prediction
Session: Understanding K-nearest neighbour model
(To be assessed through Practicals)
Introduction Session: Introduction to Computer Vision
Session: Applications of CV
Concepts of Computer Vision
Session & Activity: Understanding CV Concepts
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
Neural Network
Session: Introduction to CNN
Session: Understanding CNN
Layers of CNN
Activity: Testing CNN
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

Introduction Session: Introduction to Model Evaluation
Confusion Matrix Session & Activity: Confusion Matrix
Evaluation Score
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 ) :


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?
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)
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 

Non-technical explanation of deep learning coursera-ai-for-everyone
Unit 3: Maths for AI
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 )
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
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


Unit 6: Critical and Creative thinking ( Skills )
Design thinking framework
Right questioning ( 5W and 1H )
Identifying the problem to solve
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
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*
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
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?
- 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?
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,

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 dataset
NOTE: UNITS 2, 5, 6, 7 & 10 should be assessed through Practicals only and should not be assessed with the Theory Exam. 

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 datasets – 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
Lifecycle of an AI model

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
Student Project Work
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


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

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