Er Yogendra Singh Rajput November 09, 2025

Advanced AI FDP Syllabus

Rajaram Institute & Study For Next Present

ADVANCED AI FACULTY DEVELOPMENT PROGRAM

A 2-Day Syllabus for Applied and Next-Generation Artificial Intelligence

Conceptual image showing interconnected AI models and data pipelines

🧠 DAY 1 – Core AI, Data Science & Deep Learning Mastery

This day focuses on building a robust theoretical and practical foundation in classical Machine Learning and modern Deep Learning workflows, essential for any AI educator.

1. The AI Ecosystem & Career Paths 📈

Clear differentiation between AI, ML, DL. Understanding the industry landscape and academic career progression in AI.

2. Practical ML Workflow & Scikit-learn ⚙️

Supervised vs. Unsupervised Learning paradigms. Hands-on application of Scikit-learn for Classification/Regression tasks.

3. Advanced Data Prep & Feature Engineering 🧹

Handling Imbalanced Data (SMOTE) and Dimensionality Reduction (PCA) for model efficiency and generalization.

4. Fundamentals of Deep Learning & Keras 🧠

Working with FNNs, Activation/Loss functions. Practical introduction to TensorFlow/Keras basics.

5. Robust Model Validation & Optimization 🔬

Strategies for preventing Overfitting. Automated Hyperparameter Tuning methods and advanced validation techniques.

🤖 DAY 2 – Cutting-Edge Applications & AI Deployment (MLOps)

The second day focuses on industry-demanded skills—Generative AI, Computer Vision, Autonomous Agents, and the essential step of Model Deployment.

1. Advanced NLP: Transformers & BERT 💬

Deep dive into the Transformer Architecture. Using pre-trained models like BERT for advanced text tasks.

2. Generative AI: LLMs, Prompting & LoRA ✨

Mastering Advanced Prompting techniques. Overview of Model Fine-Tuning (LoRA) for custom LLM deployment.

3. Computer Vision (CV) Essentials 👁️

Hands-on with CNNs. Introduction to Object Detection frameworks and practical image analysis.

4. Agentic AI & Goal-Driven Systems 🎯

Principles of Autonomous AI Agents. Building practical agents using frameworks like LangChain for complex task execution.

5. Model Deployment and MLOps 🌐

Turning trained models into services: Building web apps using Streamlit or Flask. Introduction to the MLOps lifecycle.

6. Responsible AI, Ethics, & IoT Convergence ♻️

Addressing Bias and Fairness in models. Exploring the convergence of AI with Edge Computing and IoT data analytics.

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Advanced AI Technology

🧠 DAY 1 – Core AI, Data Science & Deep Learning Mastery

1. The AI Ecosystem & Career Paths 📈

2. Practical ML Workflow & Scikit-learn ⚙️

3. Advanced Data Prep & Feature Engineering 🧹

4. Fundamentals of Deep Learning & Keras 🧠

5. Robust Model Validation & Optimization 🔬

🤖 DAY 2 – Cutting-Edge Applications & AI Deployment (MLOps)

1. Advanced NLP: Transformers & BERT 💬

2. Generative AI: LLMs, Prompting & LoRA ✨

3. Computer Vision (CV) Essentials 👁️

4. Agentic AI & Goal-Driven Systems 🎯

5. Model Deployment and MLOps 🌐

6. Responsible AI, Ethics, & IoT Convergence ♻️