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دوراتنا
Emerging Trends & Technologies in the Virtual K-12 Classroom
Welcome to Emerging Trends and Technologies in K-12! This course will help you evaluate and implement technology in the most useful way within your virtual classroom setting. We will discuss the possibilities and challenges of using technology in fully online environments, although many of the ideas can be applied to blended and traditional environments as well. This course is focused on emerging trends and technologies, specifically for K-12 instructors. Most of the content will be aimed at high school-aged students in the U.S., or ages 13-18 in other countries.
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Nanotechnology and Nanosensors, Part1
Nanotechnology and nanosensors are broad, interdisciplinary areas that encompass (bio)chemistry, physics, biology, materials science, electrical engineering and more. The present course will provide a survey on some of the fundamental principles behind nanotechnology and nanomaterials and their vital role in novel sensing properties and applications. The course will discuss interesting interdisciplinary scientific and engineering knowledge at the nanoscale to understand fundamental physical differences at the nanosensors.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Nanotechnology and Nanosensors, Part 2
Learn about novel sensing tools that make use of nanotechnology to screen, detect and monitor various events in personal or professional life. Together, we will lay the groundwork for infinite innovative applications, starting from diagnosis and treatments of diseases, continuing with quality control of goods and environmental aspects, and ending with monitoring security issues. _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Nanotechnology and nanosensors are broad, interdisciplinary areas that encompass (bio)chemistry, physics, biology, materials science, electrical engineering and more.
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
Machine Learning Capstone
This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate. In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية