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CP2020 - Machine Learning

In this course, students will delve into the broad applications of machine learning. They will learn to identify and address complex problems using machine learning pipelines. They will gain hands-on experience in essential stages of these pipelines, including data preprocessing, feature engineering, and data splitting, along with the selection and tuning of models.
 
The curriculum will encompass both supervised and unsupervised machine learning tasks, such as classification, regression, clustering, and anomaly detection. Students will also familiarize themselves with various machine learning algorithms such as Logistic Regression, Support Vector Machines, k-nearest neighbors, Decision Trees, Random Forest, and XGBoost.
 
Key concepts of model optimization, such as the bias/variance trade-off and hyperparameter tuning, will be discussed in detail. By the end of the course, students will be able to create and fine-tune machine learning models, paving the way for effective data-driven problem-solving.
 

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This course is offered in the following programs:
Data Analytics  | 

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