Introduction to Machine Learning

Training Provider: DIGIPEN INSTITUTE OF TECHNOLOGY SINGAPORE PTE. LTD.
Course Reference: TGS-2023037845
S$1,750
Original: S$3,500
Save S$1,750

About This Course

Upon successful completion of the module, the trainee will be able to perform the following specific tasks:
• Gain proficiency in key machine learning algorithms, such as linear regression, logistic regression, decision trees, support vector machines, and k-means clustering, along with their associated techniques for optimization and regularization.
• Apply machine learning algorithms to real-world problems, demonstrating the ability to choose the appropriate method, pre-process data, train models, evaluate performance, and interpret results.
• Enhance problem-solving and critical thinking skills by analyzing case studies, engaging in hands-on exercises, and participating in discussions that foster a deeper understanding of the underlying principles and practical considerations in the field of machine learning.

What You'll Learn

This module provides a comprehensive introduction to the fundamental concepts and techniques of artificial intelligence and machine learning. Throughout the module, students will explore a variety of machine learning algorithms, including linear regression, regularization technique, logistic regression, decision trees, support vector machines, and k-means clustering. Each algorithm will be accompanied by real-world applications to help learners gain practical experience in solving problems using machine learning. The course is structured with a balance between theoretical understanding and hands-on exercises, culminating in a final examination to evaluate the students' grasp of the material. By the end of the module, students will have a solid foundation in machine learning, enabling them to apply these techniques effectively in various domains.

Entry Requirements

Diploma with 2 years of working experience.

Course Details

Duration 105 hours
Language English
Training Commitment Full Time
Total Enrolled 57 students
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