Machine Learning in Research: From Fundamentals to Advanced Applications

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)

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