Our Trainings
Supervised ML Capstone
Synopsis
This training program is designed to provide participants with a comprehensive understanding of supervised machine learning techniques and their practical applications. This course will cover the fundamental concepts of supervised learning, including data preprocessing, model selection, training, evaluation, and optimization. Participants will engage in hands-on projects to apply these concepts to real-world datasets, culminating in a capstone project where they will develop and present a supervised learning model. By the end of the training, participants will have the skills and confidence to tackle complex machine learning problems and implement effective solutions in their respective fields.
Objectives
Equip participants with a thorough understanding of supervised learning concepts, including classification and regression techniques, and the importance of data preprocessing. Provide practical experience using popular machine learning libraries and tools such as Scikit-learn, TensorFlow, and Keras to develop and evaluate models. Teach participants how to select the appropriate machine learning models for different types of problems and evaluate their performance using various metrics and validation techniques. Enable participants to apply their knowledge by developing a supervised learning model on a real-world dataset, including data preparation, model training, hyperparameter tuning, and presenting their findings in a professional manner.
Methodology
Ice Breaking, Presentation, Hands-on Lab Activity, Brainstorming, Discussion And Q&A.
