✅ Advanced Machine Learning: Reinforcement learning, ensemble methods, and optimization techniques
✅ Deep Learning & Neural Networks: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers
✅ Natural Language Processing (NLP): Sentiment analysis, chatbots, and language modeling
✅ Computer Vision: Image classification, object detection, and face recognition
✅ AI Model Deployment: Deploying AI models using Flask, FastAPI, or TensorFlow Serving
✅ Ethics & Bias in AI: Understanding fairness, explainability, and responsible AI development
✅ Hands-on Projects: Developing AI applications in healthcare, finance, and automation

✅ Introduction to AI & Data Science: Understanding core concepts, applications, and future trends
✅ Python for AI & Data Science: Working with NumPy, Pandas, and Matplotlib for data handling and visualization
✅ Data Preprocessing & Feature Engineering: Cleaning and preparing data for AI models
✅ Machine Learning Fundamentals: Supervised and unsupervised learning using Scikit-Learn
✅ Deep Learning Basics: Introduction to neural networks and TensorFlow/Keras
✅ Data Visualization & Analytics: Identifying insights using Matplotlib, Seaborn, and Power BI/Tableau
✅ Real-World Applications: Working on AI-driven projects such as sentiment analysis, fraud detection, and image recognition

✅ Data Science Fundamentals: Data collection, preprocessing, and feature engineering
✅ Machine Learning Techniques: Supervised and unsupervised learning models
✅ Deep Learning Basics: Understanding neural networks and AI model training
✅ Data Visualization: Exploring trends and patterns using Matplotlib and Seaborn
✅ Python for AI & Data Science: Using NumPy, Pandas, Scikit-Learn, and TensorFlow
✅ Real-World AI Applications: Building projects in image recognition, sentiment analysis, and

✅ Introduction to AI: Understanding AI, machine learning, and deep learning
✅ Python for AI: Basics of Python programming and libraries like NumPy & Pandas
✅ Supervised vs. Unsupervised Learning: Understanding different learning techniques
✅ Neural Networks & Algorithms: Basics of how AI models learn and make decisions
✅ Hands-On Projects: Building simple AI models for text, images, and predictions
✅ Ethics & AI: Understanding responsible AI and its impact on society

✅ Introduction to App Development: Understanding mobile platforms and frameworks
✅ User Interface (UI) Design: Creating visually appealing and user-friendly apps
✅ Fundamentals of Programming: Learning basic coding concepts (JavaScript, Dart, or Swift)
✅ Building Functional Apps: Developing simple, interactive applications
✅ Debugging & Testing: Ensuring app performance and usability
✅ Deployment Basics: Running apps on simulators and real devices

✅ Advanced Robotics & Automation: Designing and programming autonomous robots
✅ IoT & Cloud Integration: Connecting robots to the cloud for remote monitoring and control
✅ Wireless Communication: Using Wi-Fi, Bluetooth, and MQTT for real-time data exchange
✅ AI & Machine Learning in Robotics: Implementing basic AI models for smart decision-making
✅ Embedded Systems & Microcontrollers: Working with ESP8266, Raspberry Pi, or Arduino
✅ Hands-on Projects: Building IoT-enabled robots for smart applications

• Programming with Python
• ROBOTICS Studio
• IoT Connection
• AI World

• App Development with MIT App Inventor
• Programming with Python
• Creativity with Web Designing

• App Development with MIT App Inventor
• Programming with Python
• Creativity with Web Designing
• Robotics Studio

• Mastering MS-Office
• Logic building with Scratch
• App Development Studio: with MIT App Inventor

✅ Introduction to AI & Machine Learning: What is AI, ML, and how it’s changing the world
✅ Python Basics for AI: Learn Python fundamentals—variables, loops, functions, and logic
✅ Working with Data: Load, clean, and analyze datasets using pandas and NumPy
✅ Data Visualization: Use matplotlib and seaborn to tell stories with data
✅ Core Machine Learning Concepts: Learn supervised vs unsupervised learning
✅ Popular ML Algorithms: Explore decision trees, k-NN, and linear regression
✅ Model Building in Scikit-learn: Train, test, and evaluate your first ML models
✅ Hands-on with Real Datasets: Work on beginner-friendly datasets (Titanic, Iris, etc.)
✅ Intro to Neural Networks: Get a peek into deep learning with simple neural nets
✅ Ethics in AI: Understand fairness, bias, and responsible AI development
✅ Mini Projects: Predict outcomes, classify images, or detect fake news
✅ Capstone Project: Build and present your own end-to-end AI-powered solution
✅ Collaboration & Code Sharing: Work in teams using Jupyter Notebooks and GitHub
✅ AI Showcase & Feedback: Present projects and receive mentorship feedback
✅ No Experience Needed: Designed for beginners with step-by-step guidance

✅ Intro to Android Development: Understand mobile apps, Android ecosystem & tools
✅ Setting Up the Environment: Install Android Studio and explore the IDE
✅ Java for Android: Learn core Java concepts: variables, conditionals, loops, OOP
✅ UI Design with XML: Design clean and interactive layouts using XML in Android Studio
✅ Activity Lifecycle: Understand how Android apps run and manage screen changes
✅ User Input & Navigation: Add buttons, text fields, and multi-screen navigation
✅ Data Storage: Use SharedPreferences and SQLite for saving user data
✅ Working with APIs: Fetch and display data from the internet using JSON and REST APIs
✅ Media & Sensors: Add audio, video, and use device sensors like GPS & accelerometer
✅ Debugging & Testing: Learn to troubleshoot, test, and polish your apps
✅ Mini Projects: Build simple apps like to-do lists, quiz apps, or calculators
✅ Play Store 101: Learn the basics of publishing an app on the Google Play Store
✅ Capstone App Project: Design and develop your own unique Android app
✅ App Showcase: Present your app to mentors, peers, and receive feedback
✅ Beginner-Friendly: No prior experience needed — just passion and creativity!

✅ Introduction to Data Science: What is data science and why it matters in today’s world
✅ Data Collection & Cleaning: Learn how to gather, clean, and prepare real-world data
✅ Exploratory Data Analysis (EDA): Use graphs, charts, and stats to uncover hidden patterns
✅ Spreadsheets to Python: Transition from Excel to Python using pandas and Jupyter notebooks
✅ Data Visualization: Create eye-catching visual stories with matplotlib, seaborn, and plotly
✅ Statistics Made Fun: Understand mean, median, mode, correlation, and probability
✅ Intro to Machine Learning: Use beginner-friendly models like decision trees and k-NN
✅ Solve Real-Life Mysteries: Analyze datasets from sports, music, environment, and more
✅ Mini Projects: Hands-on projects like predicting grades or analyzing social media trends
✅ Ethics in Data Science: Discuss bias, privacy, and responsible data use
✅ Storytelling with Data: Turn insights into presentations and data dashboards
✅ Collaborative Challenges: Team up to crack data puzzles and win mini-hackathons
✅ Capstone Project: Investigate a real-world question and present your findings
✅ Demo Day & Certificates: Showcase your work to mentors, parents, and peers
✅ No Prior Coding Needed: Learn everything from the ground up — just bring curiosity!

✅ Introduction to Smart Robotics: Basics of robotics, automation, and intelligent systems
✅ Robot Hardware Components: Sensors, actuators, microcontrollers, and power systems
✅ Programming Foundations: Coding robots using Python or Arduino (C++)
✅ Motor & Sensor Control: Hands-on with motors, IR, ultrasonic, and light sensors
✅ Building Smart Robots: Create line-followers, obstacle avoiders, and reactive bots
✅ Wireless & IoT Integration: Control robots via Bluetooth, Wi-Fi, or web apps
✅ Automation Logic & Decision Making: Implement feedback loops and smart behavior
✅ Introduction to AI in Robotics: Optional add-on using computer vision and ML basics
✅ Robot Simulation Tools: Use virtual platforms like TinkerCAD or Webots
✅ Autonomous Systems: Explore navigation, mapping, and real-time adjustments
✅ Mini Projects: Build simple automation systems and smart gadgets
✅ Team Collaboration: Work in groups to solve real-world robotic challenges
✅ Capstone Project: Design, code, and demo your own smart robot
✅ Showcase & Presentation: Present projects to peers and mentors
✅ Certificates & Recognition: Earn a certificate of completion and prizes for top teams