
AI for Psychological and Behavioral Analysis
Bridging AI and Psychology: Building Smarter Behavioral Models
Skills you will gain:
About Program:
AI for Psychological and Behavioral Analysis” is a hands-on 3-day workshop designed to introduce participants to the practical applications of AI in understanding human behavior, emotions, and mental health. Through this workshop, participants will learn to use tools like Python, TensorFlow, and Power BI to build AI models for psychological data analysis.
Aim: The aim of the “AI for Psychological and Behavioral Analysis” workshop is to empower participants with practical AI skills for analyzing human behavior, emotions, and mental health. It covers AI tools like Python, TensorFlow, and Power BI to build emotion recognition and behavioral models. Participants will gain hands-on experience in data engineering, feature extraction, and model optimization. The goal is to equip professionals with industry-ready capabilities for AI-driven psychological research.
Program Objectives:
- Introduce AI concepts and their application in psychological and behavioral analysis.
- Teach participants to use Python, TensorFlow, and Power BI for AI model development.
- Provide hands-on experience in data engineering, feature extraction, and model optimization.
- Enable participants to apply AI techniques for emotion recognition and sentiment analysis.
- Equip participants with industry-ready skills for AI-driven psychological research and behavioral insights.
What you will learn?
📅 Day 1: Introduction to AI for Psychological & Behavioral Analysis
- AI for Psychology: AI applications in emotion recognition and sentiment analysis.
- Importance of AI: Role in modern psychological research and mental health.
- Tools & Technologies: Introduction to
Python,TensorFlow,Power BI,MLflow. - Data Sources & Workflows: Collecting and processing behavioral data.
Hands-On:
- Exercise 1: Exploratory data analysis in Google Colab (visualizing trends).
- Exercise 2: Data preprocessing for AI modeling.
📅 Day 2: Data Engineering & AI Modeling for Behavioral Analysis
- Data Engineering: Preparing datasets and feature engineering.
- Building AI Models: Machine learning models for behavioral analysis.
- Model Evaluation & Optimization: Performance metrics and improvements.
Hands-On:
- Exercise 1: Build a machine learning model using
PythonandScikit-learn. - Exercise 2: Model evaluation using **MAE** and **RMSE**.
📅 Day 3: Advanced AI Applications & Hands-On Project
- Advanced AI Techniques: Generative AI and Computer Vision for emotion recognition.
- MLOps: Deploying AI models at scale.
- AI in Behavioral Systems: Predictive maintenance and AI health estimation.
Hands-On:
- Exercise 1: Build an emotion recognition pipeline.
- Exercise 2: Develop a real-time emotion recognition system in Google Colab.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Career Supporting Skills
Program Outcomes
- Gain practical experience in applying AI for psychological and behavioral analysis.
- Build and optimize AI models for emotion recognition, sentiment analysis, and behavioral data.
- Develop hands-on skills using Python, TensorFlow, and Power BI for real-world AI applications.
- Understand data workflows, feature extraction, and model evaluation for psychological research.
- Equip participants with the capability to implement AI-driven solutions in mental health and behavioral sciences.
