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AI & ML for class 10 to 12

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Course Brochure
Last Updated: 05/2026
Start Date Jan 01, 2026
Duration 120 Days
EMI Starting At ₹10000
Language Availability Both English & Hindi

Course Key Highlights:

Hours of Instructor-Led Training
Flexible Schedule
22 Hours of Self-Paced Videos
Certification
Job Assistance
Lifetime Free Upgrade
56 Hours of Projects Exercises
Hours of Instructor-Led Training
Why should you opt for this course?
  • Build future-ready skills in AI, Python, and Machine Learning.
  • Gain hands-on experience with real projects and modern tools.
  • Strengthen logical thinking, problem-solving, and innovation.
  • Create a strong portfolio for higher studies, competitions, and careers.

What will you learn?

  • Understand AI, Machine Learning, and real-world applications..
  • Master Python programming and essential coding concepts..
  • Build AI models using data, no-code tools, and ML libraries..
  • Create projects in computer vision, chatbots, and data analysis..

Who should enroll?

. School Students (Class 11–12
. School Students (Class 11–12

Perfect for teens wanting strong AI, coding, and future technology foundations.

Beginners in Programming
Beginners in Programming

Ideal for students starting Python and seeking structured hands-on learning experience.

Tech-Focused Learners
Tech-Focused Learners

Great for those interested in AI projects, innovations, and real-world applications.

What are the prerequisites?

  • Basic computer operation skills and comfort using the internet.
  • Interest in technology, problem-solving, and learning Python or AI concepts.

What is the price?

₹15000 ₹10000
₹10000 Buy Now

Course Curriculum

⭐ MODULE 1: Foundations of AI & Python Programming (Week 1–2) 10 lectures

🎯 Learning Outcomes

  • Understand AI, ML, DL, Data Science basics
  • Learn fundamentals of Python coding
  • Understand data, algorithms, logic building
  • Execute simple programs and build rule-based AI

📘 Topics

1. Introduction to AI

  1. What is AI?
  2. Types of AI: ANI, AGI, ASI
  3. Real-life applications (health, finance, education, IT)
  4. AI vs ML vs DL vs Data Science
  5. AI misconceptions

2. Introduction to Data

  1. What is data?
  2. Features, labels
  3. Structured vs unstructured
  4. Dataset quality

3. Python Essentials

  1. Input/output
  2. Variables & data types
  3. Operators
  4. Conditions (if–else)
  5. Loops
  6. Functions
  7. Lists, dictionaries
  8. File handling (txt/csv)

🧪 Hands-On Labs

  1. Python Calculator
  2. Marks Analyzer
  3. Rule-based Chatbot
  4. Pattern Generator
  5. CSV Reader

🛠 Mini Project

“School Information Chatbot” – rule-based logic using Python

⭐ MODULE 2: Machine Learning Foundations (Week 3–4) 10 lectures

🎯 Learning Outcomes

  • Understand ML concepts & workflow
  • Build supervised and unsupervised models
  • Train, test, validate using datasets
  • Use Teachable Machine and scikit-learn

📘 Topics

1. ML Basics

  1. What is ML?
  2. Supervised vs Unsupervised vs Reinforcement
  3. ML workflow (Collect → Clean → Train → Test → Improve)
  4. Features, labels, target variable

2. Supervised Learning

  1. Regression
  2. Classification
  3. Models:
  4. Linear Regression
  5. Logistic Regression
  6. Decision Trees
  7. KNN

3. Unsupervised Learning

  1. Clustering
  2. K-Means
  3. Pattern discovery

4. Model Evaluation

  1. Accuracy
  2. Train-test split
  3. Overfitting & underfitting

🧪 Hands-On Labs

  1. Teachable Machine: Image Classifier
  2. Teachable Machine: Pose/Sound Model
  3. Python ML: Iris Classifier
  4. Python ML: Marks Prediction

🛠 Mini Project

“Student Score Predictor” using Linear Regression

⭐ MODULE 3: Data Science & Data Visualization (Week 5–6) 10 lectures

🎯 Learning Outcomes

  • Work with datasets using Pandas & Numpy
  • Visualize data using Matplotlib
  • Clean, transform & analyze real datasets
  • Build small analytics dashboards

📘 Topics

1. Data Handling

  1. Importing datasets
  2. Missing values
  3. Outlier detection
  4. Data types

2. Data Analysis with Pandas

  1. Filtering, sorting
  2. Groupby, aggregation
  3. Merging data

3. Data Visualization

  1. Bar, line, pie charts
  2. Scatter plots
  3. Histograms

4. Basic Statistics

  1. Mean, median, mode
  2. Variance, standard deviation
  3. Correlation

🧪 Hands-On Labs

  1. Analyse “Student Performance Dataset”
  2. Create Visual Dashboard
  3. CSV Analysis: Attendance, Fees, Sales

🛠 Mini Project

“School Survey Data Dashboard” using Pandas + Matplotlib

⭐ MODULE 4: Deep Learning & Neural Networks (Week 7) 5 lectures

🎯 Learning Outcomes

  • Understand neural networks, layers, weights
  • Learn ANN fundamentals
  • Train a simple deep learning model
  • Understand activations, loss, and optimizer roles

📘 Topics

1. What is Deep Learning?

  1. ANN vs CNN
  2. Perceptron basics
  3. Neurons, hidden layers

2. ANN Structure

  1. Activation functions (ReLU, sigmoid)
  2. Loss functions
  3. Optimizers

3. Training Neural Networks

  1. Epochs
  2. Batch size
  3. Overfitting

🧪 Hands-On Labs

  1. Neural Network with TensorFlow (simple)
  2. Train MNIST digit recognition model

🛠 Mini Project

“Digit Recognizer” using TensorFlow/Keras

⭐ MODULE 5: AI Applications — CV, NLP & Chatbots (Week 8–9) 10 lectures

🎯 Learning Outcomes

  • Understand computer vision
  • Build text classification projects
  • Create working chatbots
  • Train simple CV models

📘 Topics

1. Computer Vision

  1. Image arrays
  2. Preprocessing images
  3. Basic OpenCV functions
  4. Edge detection

2. NLP — Natural Language Processing

  1. Tokenization
  2. Stopwords
  3. Stemming
  4. Sentiment analysis

3. Chatbots

  1. Rule-based chatbots
  2. ML-powered chatbots
  3. NLP integration

🧪 Hands-On Labs

  1. CV: Face Detection (pre-trained model)
  2. NLP: Sentiment Analyzer
  3. Text Classifier: Spam vs Non-Spam
  4. Chatbot using Python

🛠 Mini Project

“Student Helpdesk Chatbot” OR “Sentiment Analysis App”

⭐ MODULE 6: Responsible AI, Ethics & Cyber Safety (Week 10) 5 lectures

🎯 Learning Outcomes

  • Understand responsible AI practices
  • Identify unethical AI behaviours
  • Learn cyber safety rules for AI users
  • Understand bias, fairness, transparency

📘 Topics

1. AI Ethics

  1. Privacy & data protection
  2. Bias in AI algorithms
  3. Ethical dilemmas in AI

2. Responsible AI Principles

  1. Explainability
  2. Accountability
  3. Transparency

3. AI & Cyber Safety

  1. Safe data usage
  2. Deepfake awareness
  3. Cyber threats linked to AI

🧪 Hands-On Labs

  1. Case Study: AI Bias Example
  2. Activity: Rewrite data to remove bias
  3. Create “Responsible AI Pledge” poster

🛠 Mini Project

“Ethical AI Report: Impact of AI on Society”

⭐ MODULE 7: Capstone Projects + Portfolio + Viva (Week 11–12) 10 lectures

🎯 Learning Outcomes

  • Build a complete ML/AI project end-to-end
  • Create professional documentation
  • Present AI project to panel

📘 Capstone Project Options

Beginner

  1. Student Performance Predictor
  2. Image Classifier (Waste, Fruits, Leaves)
  3. Chatbot for School

Intermediate

  1. Fake News Classifier
  2. Weather Prediction Model
  3. Handwritten Digit Recognition

Advanced

  1. Face Mask Detector
  2. Recommendation System
  3. Emotion Detection (Text or Image)

🛠 Capstone Deliverables

  • Working model
  • Source code
  • Dataset explanation
  • Accuracy metrics
  • Project report
  • Presentation slides

Course Projects

Student Performance Prediction Model

Build a machine learning model that predicts student scores using data like study hours, attendance, and past performance—teaching real-world regression and data analysis skills.

Read More
AI-Based Image Classification System

Train an AI model to identify objects such as fruits, waste types, or handwritten digits using Teachable Machine or Python, learning how computer vision works.

Read More
Smart Chatbot for School Queries

Create an intelligent chatbot that answers student-related questions—timings, subjects, events—using Python and NLP, introducing conversational AI and automation.

Read More
Sentiment Analysis Tool for Reviews

Develop an AI tool that analyzes text (reviews, feedback, comments) and classifies emotions as positive, negative, or neutral—excellent for learning NLP fundamentals.

Read More

Tools Covered

Python Programming
Google Teachable Machine
Scikit-Learn
Pandas & Matplotlib

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Your Instructors

Person

Abhinav Thakur

Senior Full Stack Developer with 10+ years of extensive experience in designing, developing, and deploying scalable web applications across modern tech stacks. Skilled in leading projects, mentoring teams, and delivering high-quality solutions.