Machine Learning in Research: From Fundamentals to Advanced Applications

This workshop bridges the gap between theoretical ML knowledge and its application in academic research. Participants will learn about supervised and unsupervised learning, deep learning, and advanced ML algorithms, with hands-on projects tailored for research applications. By the end of the course, participants will be able to effectively use ML tools to analyze complex datasets, automate research workflows, and derive meaningful insights.

• Understand and apply ML algorithms across diverse research fields.
• Develop skills in advanced ML and deep learning models for research applications.
• Master data preprocessing and feature engineering for improved model accuracy.
• Implement ML-based research projects with hands-on experience.
• Enhance data-driven research by integrating ML insights and automations.

1. Machine Learning Foundations
   • Overview of ML for academic research
   • Types of learning: Supervised, Unsupervised, and Reinforcement Learning
2. Data Preparation for ML
   • Data transformation, normalization, and feature selection
   • Handling imbalanced datasets
3. Core Algorithms for Research Applications
   • Linear regression, decision trees, k-nearest neighbors, support vector machines (SVM)
   • Model evaluation techniques: Precision, Recall, F1 Score
4. Model Optimization
   • Hyperparameter tuning and model selection
   • Cross-validation techniques
5. Deploying ML Models for Research
   • Practical tools for deploying models in research
   • Hands-on experience with model deployment

Weeks wise Schedule:

• Module 1: Introduction to Machine Learning and Data Preparation
  • ML concepts for research applications
  • Hands-on data preparation: Feature selection, data cleaning
• Module 2: Machine Learning Algorithms and Applications
  • Practical session: Applying core ML algorithms on research datasets
• Module 3: Model Optimization and Hyperparameter Tuning
  • Hands-on: Tuning models for optimal research outcomes
• Module 4: Model Deployment in Research
  • Deploying ML models for real-world research using Python (Flask, Streamlit)