Essentials of Machine Learning (Asynchronous e-learning)
About This Course
Upon completion of this course, participants will be able to:
• Demonstrate the ability to extract, clean, and prepare data for machine learning applications by employing appropriate data preprocessing techniques
• Analyze and interpret patterns in datasets by utilizing data visualization tools and techniques for data exploration
• Develop and implement supervised learning models to solve regression and classification problems effectively
• Synthesize knowledge of unsupervised techniques to analyze data and identify underlying structures or relationships
• Critically evaluate the performance of machine learning models by applying relevant evaluation metrics and methodologies
What You'll Learn
Throughout the course, participants will learn the importance of data preparation and exploration, as well as how to effectively use data visualization techniques. They will gain hands-on experience in applying supervised learning for regression and classification tasks, and delve into unsupervised methods for data analysis. Additionally, attendees will learn how to evaluate the performance of machine learning models to ensure their accuracy and reliability.
Upon completing this course, participants will be well-equipped to confidently apply machine learning techniques in their professional or academic pursuits. This foundational knowledge can be a significant asset for those aiming to advance their careers, improve their problem-solving capabilities, or drive innovation in their organizations. By mastering the fundamentals of machine learning, attendees will be better prepared to harness the power of data and stay at the forefront of technological advancements.
Entry Requirements
• Basic programming knowledge: Participants should possess a fundamental understanding of programming concepts and be familiar with at least one programming language, preferably Python, as it is widely used in the field of machine learning
• Foundational knowledge in mathematics and statistics: Attendees should have a basic understanding of linear algebra, calculus, probability, and statistics, as these concepts form the foundation of many machine learning algorithms and techniques