Syllabus Duration: 25 Weeks

Module-1 : Introduction to Artificial Intelligence

Topics:

• What is AI? Understanding its definition and scope.
• Subfields: Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Computer Vision
• Mimicking in AI: How AI replicates human tasks (e.g., language generation, as in LLMs).
• Real-world applications: Chatbots, virtual assistants, and graphics (e.g., Sharma’s work on 3D avatars for sign language).

Activities:

• Watch introductory videos or podcasts (e.g., Zero AI Podcast with Paritosh and Sahil Sharma).
• Explore case studies (e.g., AI in virtual reality or IoT health monitoring).

Learning Outcome: Understand AI’s role and how it mimics human behaviour, with examples from Sharma’s work

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Module-2 : Basics of Python for AI

Topics:

• Python fundamentals: Syntax, variables, loops, and functions
• Key libraries: NumPy (numerical operations), Pandas (data handling), Matplotlib (visualization).
• Writing simple scripts for data processing or visualization

Activities:

• Install Python and set up an environment (e.g., Google Collab or Jupiter Notebook).
• Code exercises: Create a simple data plot or process a dataset (e.g., health data for IoT applications).

Learning Outcome: Gain basic programming skills to support AI tasks, reflecting Sharma’s programming foundation from SRM Institute

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Module-3: Machine Learning Fundamentals

Topics:

• Supervised Learning: Regression (e.g., predicting values) and classification (e.g., spam detection).
• Unsupervised Learning: Clustering (e.g., grouping customers).
• Model evaluation: Accuracy, precision, recall
• Tools: Scikit-learn for basic ML models.

Activities:

• Build a simple model (e.g., predict house prices using regression).
• Use datasets from Kaggle to practice ML workflows.

Learning Outcome: : Understand and implement basic ML algorithms, foundational to Sharma’s machine learning expertise

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Module-4: Introduction to Generative AI and LLMs

Topics:

• What are Large Language Models (LLMs)? How they mimic human language (e.g., ChatGPT, Grok).
• Prompt engineering: Crafting effective inputs for LLMs to generate text or answers.
• Generative models in graphics: Basics of GANs (Generative Adversarial Networks), relevant to Sharma’s work on 3D avatars and virtual reality

Activities:

• Experiment with free LLMs (e.g., Hugging Face models) to generate text or answers.
• Try basic prompt engineering (e.g., “Summarize a paragraph” or “Generate a story”).
• Explore Blender for simple 3D graphics (aligned with Sharma’s research).

Learning Outcome: Learn to use LLMs and understand generative AI, connecting to Sharma’s focus on deep generative approaches

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Module-5: AI in Real-World Applications

Topics:

• AI in graphics: Animating 3D avatars (e.g., sign language synthesis, as in Sharma’s Ph.D.).
• AI in IoT: Applications like health monitoring (e.g., Vitamin D deficiency detection, as pitched by Sharma).
• Ethical AI: Addressing bias and fairness in AI models.

Activities:

• Case study: Analyze how AI is used in virtual reality or IoT.
• Mini project: Create a chatbot or a simple graphic visualization using Python and Blender.

Learning Outcome: Apply AI to practical scenarios, inspired by Sharma’s work in accessibility and graphics.

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Module-6: Hands-On AI Project

Topics:

• Project planning: Define a problem, collect data, and choose tools.
• Example projects.
  1. Build a chatbot using an LLM API (e.g., Hugging Face).
  2. Create a simple 3D avatar animation using Blender and Python.
  3. Develop an IoT-based health prediction model (e.g., using mock health data)

Activities:

• Use Kaggle datasets or open-source tools to build a project.
• Share projects on GitHub or the Zero AI app for feedback.

Learning Outcome: Develop a portfolio project showcasing AI skills, aligning with Zero Ai’s focus on practical growth.

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Module-7: AI Deployment and Future Trends.

Topics:

• Deploying AI models: Using Stream lit or cloud platforms (e.g., Google Cloud).
• AI trends: Advances in LLMs, generative AI, and virtual reality.
• Career paths: Roles like AI engineer, data scientist, or graphics programmer

Activities:

• Deploy a simple AI model (e.g., a chatbot) as a web app using Stream lit.
• Attend Zero Ai’s live workshops for career guidance.

Learning Outcome: Understand how to deploy AI solutions and explore career opportunities.