PadhoSikhoBanaao

Bharat ko aage badhaao

PadhoSikhoBanaao

Bharat ko aage badhaao

Learning → Career Readiness

This professional track prepares students for higher education and global opportunities. They develop AI/ML models, full robotics systems, Python coding, and portfolio-grade projects to stand out in the tech world.

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Course Highlights (Aligned with NEP)

Artificial Intelligence (Advanced Concepts)

  1. AI systems, agents, and architectures
  2. Model training lifecycle: data → preprocessing → model → evaluation → deployment
  3. Industry AI: healthcare, banking, telecom, autonomous systems, cybersecurity
  4. AI ethics: bias, fairness, transparency, safety frameworks
  5. Introduction to AI model deployment

Data Science & Analytics

  1. Data pipelines
  2. NumPy, Pandas (in-depth)
  3. Exploratory Data Analysis (EDA)
  4. Data cleaning, wrangling
  5. Data visualization using Matplotlib
  6. Mini-projects: Sales analysis, Financial data forecasting (basic)


Deep Learning & Neural Networks (Intro Level)

  1. Neural networks basics
  2. Perceptron, activation functions
  3. Simple ANN using beginner-friendly tools
  4. Projects: Image classifier, Handwritten digit recognition

Natural Language Processing (NLP)

  1. Text preprocessing, tokenization, stop words
  2. Sentiment analysis
  3. Keyword extraction
  4. Build projects: Chatbots, Email classifier, Resume analyzer


Internet of Things (IoT) & Cloud

  1. IoT architecture
  2. Sensors, actuators, edge computing
  3. Introduction to cloud platforms
  4. IoT projects: Smart home control, Weather monitoring system, IoT automation dashboard

Cybersecurity Basics

  1. Threats, malware, phishing
  2. Password encryption basics
  3. Network security fundamentals
  4. Ethical usage of digital tools

Python Programming (Core + Advanced)

  1. Advanced data types, file handling
  2. Error handling, OOP concepts
  3. Modules, packages, virtual environments
  4. API usage and JSON handling
  5. Projects: Weather app using API, File encryption tool, Student management system (OOP)

Machine Learning (ML) – Practical & Theory

  1. Supervised, Unsupervised ML
  2. Linear regression, logistic regression
  3. KNN, SVM, Decision Trees, Random Forest — simple implementations
  4. Train-test split, cross-validation
  5. Model evaluation metrics
  6. Hands-on ML projects:  House price prediction, Email spam detection, Diabetes detection (demo dataset)

Computer Vision (Intermediate Level)

  1. Image preprocessing, filters
  2. OpenCV fundamentals
  3. Feature extraction
  4. Object detection basics
  5. Projects: Face detection system, Traffic signal detection demo

     

Robotics & Embedded Systems

  1. Arduino / Raspberry Pi / Microcontrollers
  2. Motors, relays, actuators
  3. Wireless modules – Bluetooth, WiFi
  4. Robotics projects:  Home automation robot, Voice-controlled robot, Surveillance bot

Generative AI (Beginner Friendly)

  1. What is GenAI?
  2. Prompting, transformers
  3. Applications: text, image, code generation
  4. Build projects: AI-assisted storytelling, AI design generator, Chat-based GenAI applications

Design Thinking + Innovation Portfolio

  1. Problem identification
  2. Prototype building
  3. Real-world solution design
  4. Portfolio creation for college admissions

YEAR-LONG PROJECT WORK

Students will build a professional-grade portfolio with:

✔ AI/ML models
✔ Python applications
✔ Robotics prototypes
✔ IoT systems
✔ Computer Vision/NLP demos
✔ Personal tech portfolio website (optional)

Learning Outcomes

Students will be able to:

  1. Build & apply AI/ML systems
  2. Code confidently in Python (Advanced)
  3. Work with data analytics and visualization
  4. Develop robotics & IoT solutions
  5. Create CV/NLP-based projects
  6. Understand cybersecurity basics
  7. Build college-ready & job-ready portfolios
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