Class 9

Course Objectives
Instructor
The Kavach
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Course-cyber

Chapter 1: Introduction to AI & ML
1. What is intelligence? Demystifying “artificial.”
2. Key AI terms: robots, algorithms, data, learning.
3. Real-world AI examples: facial recognition, virtual assistants, self-driving cars.
4. Introduction to basic programming logic using Python (variables, loops, conditions).
5. Activity: Code a simple AI-like behavior (e.g., rock-paper-scissors game, number guessing).

Chapter 2: Types of AI & ML
1. Supervised learning: learning from labeled data (classification, regression).
2. Unsupervised learning: finding patterns in unlabeled data (clustering, dimensionality reduction).
3. Reinforcement learning: learning through trial and error.
4. Introduction to data acquisition and preprocessing.
5. Activity: Identify different AI types in video games or apps, collect sample data, and predict outcomes.

Chapter 3: Applications of AI & ML
1. AI in healthcare: diagnosis, personalized medicine, drug discovery.
2. AI in education: adaptive learning, intelligent tutoring systems.
3. AI in entertainment: movie recommendations, music generation, game AI.
4. AI in environment: monitoring climate change, optimizing sustainable practices.
5. Activity: Research and present an AI application in a domain you’re interested in.

Chapter 4: Building an AI System
1. Understanding and choosing different data types (numerical, text, images).
2. Exploring data visualization techniques with tools like Tableau or Google Data Studio.
3. Introduction to Python libraries for AI: NumPy, pandas, scikit-learn.
4. Building a simple chatbot or image classifier using Python and pre-trained models.
5. Activity: Collect data on a topic you choose, clean and preprocess it, and build a basic AI model to analyze it.

Chapter 5: Introduction to Machine Learning Models
1. Linear regression for modeling relationships between variables.
2. Classification algorithms: decision trees, k-nearest neighbors, support vector machines.
3. Evaluation metrics for ML models: accuracy, precision, recall, F1-score.
4. Understanding the importance of data quality and bias in ML.
5. Activity: Build an ML model to predict movie ratings or song popularity using sample datasets.

Chapter 6: Ethics and Societal Impact of AI
1. Algorithmic bias and fairness: understanding and mitigating its effects.
2. Privacy concerns and data security in AI systems.
3. The future of work and automation: potential job displacement and upskilling needs.
4. Responsible AI development principles: transparency, accountability, explainability.
5. Activity: Debate the ethical implications of a specific AI application (e.g., facial recognition in public spaces).

Chapter 7: Natural Language Processing (NLP)
1. Introduction to NLP: understanding and manipulating text data.
2. Basic NLP tasks: sentiment analysis, topic modeling, named entity recognition.
3. Tools and libraries for NLP: spaCy, NLTK, TensorFlow Hub.
4. Building a simple NLP application using pre-trained language models.
5. Activity: Analyze news articles or social media posts using NLP techniques to extract insights.

Chapter 8: Computer Vision
1. Introduction to computer vision: analyzing and interpreting images and videos.
2. Basic computer vision tasks: object detection, image classification, image segmentation.
3. Tools and libraries for computer vision: OpenCV, TensorFlow Lite, PyTorch.
4. Building a simple image recognition application using pre-trained models.
5. Activity: Train a model to recognize different types of flowers or animals in images.

Chapter 9: Exploring Advanced AI Techniques
1. Introduction to deep learning: artificial neural networks inspired by the brain.
2. Generative models: creating new content, like music or images, based on existing data.
3. Reinforcement learning in games: training AI agents to achieve goals through trial and error.
4. Understanding the limitations and challenges of current AI research.
5. Activity: Explore pre-trained deep learning models for image generation or text summarization.

Chapter 10: Creating Your Own AI Project
1. Identify a problem or challenge you can solve using AI.
2. Design and plan your AI project, considering data requirements, model selection, and evaluation.

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Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed