Capstone Project
The concluding module integrates knowledge gained throughout the program. Students select a problem, gather data, and build a full pipeline including model training, evaluation metrics, and deployment strategies. Techniques such as hyperparameter tuning and feature engineering strengthen outcomes. The project reflects real industry workflows and documentation standards. Students present results and justify methodological choices. This experience enhances competence and portfolio value.
Project Components:
- End-to-end ML pipeline
- Evaluation and refinement
- Deployment and presentation
Related Conference of Capstone Project
February 10-11, 2026
20th International Conference on Alzheimer's disease and Dementia
Berlin, Germany
February 25-26, 2026
15th International Conference on Herbal Medicine and Acupuncture
Aix-en-Provence, France
March 09-10, 2026
13th World Machine Learning and Deep learning Conference
Singapore City, Singapore
April 20-21, 2026
16th International Conference on Internal Medicine and Hospital Management
Amsterdam, Netherlands
May 25-26, 2026
4th Annual Meeting on Pharmaceutical Science and Drug Manufacturing and Development
Paris, France
June 01-02, 2026
14th International Conference on Predictive, Preventive and Personalized Medicine & Molecular Diagnostics
Zurich, Switzerland
June 15-16, 2026
11th International Conference on Alternative & Integrative Medicine
Zurich, Switzerland
August 24-25, 2026
6th International Conference on Medical Health Science, Pharmacology & Bio Technology
Paris, France
October 19-20, 2026
12th World Congress on Medicinal Plants and Marine Drugs
Aix-en-Provence, France
Capstone Project Conference Speakers
Recommended Sessions
- Capstone Project
- Classification Algorithms and Decision Systems
- Clustering and Unsupervised Pattern Discovery
- Convolutional Neural Networks (CNNs)
- Data Preparation and Feature Processing
- Deployment of ML Models
- Dimensionality Reduction and Feature Selection
- Ensemble Learning Approaches
- Ethics and Responsible AI
- Exploratory Data Analysis and Visualization
- Feature Engineering and Representation Design
- Foundations of Machine Learning
- Generative Models and Creativity in AI
- Introduction to Deep Learning
- Model Evaluation and Performance Validation
- Natural Language Processing Basics
- Neural Network Architecture and Optimization
- Optimization and Hyperparameter Tuning
- Recurrent Neural Networks (RNNs)
- Regression-Based Learning Models
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