The Introduction to Machine Learning course provides a comprehensive introduction to the fundamental concepts, techniques, and applications of machine learning (ML), excluding artificial intelligence (AI). This course aims to equip students with a solid foundation in ML, enabling them to understand the principles and methodologies behind ML algorithms and their practical applications in various domains.
• Participants will be able to demonstrate a comprehensive understanding of the basic concepts, terminology, and goals of machine learning.
• Participants will be able to apply various supervised and unsupervised machine learning algorithms to solve real-world problems.
• Participants will be able to evaluate and select appropriate machine learning models based on performance metrics and model evaluation techniques.
• Participants will be able to perform feature engineering and selection techniques to enhance the performance of machine learning models.
• Participants will be able to understand the ethical considerations and responsible practices in machine learning applications.
• Participants will be able to analyze and interpret the results of machine learning models and assess their impact on decision-making processes.
• Participants will be able to identify potential biases, fairness issues, and privacy concerns in machine learning algorithms.
• Participants will be able to stay informed about current trends and advancements in the field of machine learning.
• Participants will be able to apply critical thinking and problem-solving skills through hands-on exercises and projects.
• Participants will be able to communicate effectively about machine learning concepts and techniques to both technical and non-technical audiences.