SCTP Machine Learning
Training Provider: SINGAPORE INSTITUTE OF TECHNOLOGY
Course Reference: TGS-2025052832
S$1,204
Original: S$4,013
Save S$2,809
About This Course
This course equips learners with the essential knowledge and skills to apply machine learning techniques to real-world data analytics and business intelligence problems. Participants will delve into the fundamental concepts of machine learning, explore various algorithms, and gain hands-on experience in building predictive models.
What You'll Learn
The course objectives are to equip learners with knowledge and skills to:
- Define machine learning and its applications in data analytics and Business Intelligence (BI).
- Explore supervised, unsupervised, and reinforcement learning paradigms.
- Discuss the role of data preprocessing and feature engineering in machine learning.
- Learn about linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
- Understand the strengths, weaknesses, and use cases of different algorithms.
- Apply machine learning techniques to build predictive models for various business problems, such as customer churn prediction, fraud detection, and demand forecasting.
- Evaluate model performance using appropriate metrics and techniques.
- Tune model hyperparameters to optimise accuracy and efficiency.
- Leverage Machine Learning for Data-Driven Decision Making
- Integrate machine learning into data analytics and BI workflows.
- Understand the ethical implications of machine learning and data-driven decision-making.
- Define machine learning and its applications in data analytics and Business Intelligence (BI).
- Explore supervised, unsupervised, and reinforcement learning paradigms.
- Discuss the role of data preprocessing and feature engineering in machine learning.
- Learn about linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
- Understand the strengths, weaknesses, and use cases of different algorithms.
- Apply machine learning techniques to build predictive models for various business problems, such as customer churn prediction, fraud detection, and demand forecasting.
- Evaluate model performance using appropriate metrics and techniques.
- Tune model hyperparameters to optimise accuracy and efficiency.
- Leverage Machine Learning for Data-Driven Decision Making
- Integrate machine learning into data analytics and BI workflows.
- Understand the ethical implications of machine learning and data-driven decision-making.
Entry Requirements
Anyone who has intermediate level of knowledge in the use of computer applications and has some Science, Technology, Engineering, and Mathematics (STEM) background in work experience or education.
Course Details
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