Rajaram Institute & Study For Next Present
ADVANCED AI FACULTY DEVELOPMENT PROGRAM
A 2-Day Syllabus for Applied and Next-Generation Artificial Intelligence
🧠 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.
