Machine Learning for Earth System Sciences
Learn spatio-temporal statistics and data mining for earth system science with the Machine Learning for Earth System Sciences certification by Swayam.
Online
8 WeeksQuick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Machine Learning for Earth System Sciences certification course offers a comprehensive exploration of spatio-temporal statistics and data mining, focusing on their applications in earth system sciences. Intended for final-year undergraduates, postgraduates, and research students, it covers earth system observations, data analytics, and modelling in domains like hydrology and climate.
Prerequisites include mandatory machine learning knowledge, optional deep learning expertise, and a working understanding of earth system science domains. The Machine Learning for Earth System Sciences certification by Swayam caters to both undergraduate and postgraduate levels, fostering a solid grasp of statistical concepts in analysing earth system phenomena.
Also Read: Online Machine Learning Certification Courses
The Highlights
- Elective Online Course
- 8 Weeks Duration
- Offered by IIT Kharagpur
Programme Offerings
- Certificate of completion
- Practical Learning
- Hands-on Experience
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 1000 | yes | IIT Kharagpur |
The Machine Learning for Earth System Sciences certification fees is free. However, if you want a certificate, you must register and take the proctored exam at designated centres, which is optional and comes with a fee of Rs 1000.
Machine Learning for Earth System Sciences Certification Fee Structure -
Particulars | Total Fees |
Machine Learning For Earth System Sciences (exam) | Rs 1000/- |
Eligibility Criteria
Academic Qualifications
The Machine Learning for Earth System Sciences certification course is intended for students in their final year of undergrad studies, postgraduates, and research students, requiring a basic understanding of machine learning, with deep learning being optional, and familiarity with one or two earth system science domains.
Certification Qualifying Details
To receive the Machine Learning for Earth System Sciences certification by Swayam, you need a minimum average assignment score of 10/25 and an exam score of 30/75.
What you will learn
After completing the Machine Learning for Earth System Sciences certification syllabus, you will gain a thorough understanding of spatio-temporal statistics and data mining concepts, establishing a strong foundation for subsequent topics. You will also delve into the practical applications of the concepts in earth system sciences, covering hydrology, climate, and soil.
Upon completion of the Machine Learning for Earth System Sciences training, you will gain the skills to effectively analyse earth system observations, perform data analytics, and create models that contribute to a nuanced understanding of complex phenomena.
Who it is for
The Machine Learning for Earth System Sciences online course is suitable for individuals interested in the intersection of computer science and earth system sciences. This course is particularly suitable for:
Admission Details
Follow these steps to join the Machine Learning for Earth System Sciences classes:
Step 1: Browse the URL below:
https://onlinecourses.nptel.ac.in/noc23_cs86/preview
Step 2: Click on the “Sign-in/ Register” button
Step 3: Fill out the necessary details and submit the form
The Syllabus
Evaluation process
The Machine Learning for Earth System Sciences certification offers an optional exam. Your final score is determined by averaging the best 8 out of 12 assignments (25%) and your proctored exam score (75%).
IIT Kharagpur Frequently Asked Questions (FAQ's)
Mandatory prerequisites include a solid understanding of machine learning, while familiarity with deep learning is optional. Additionally, participants should have a working knowledge of earth system sciences.
The course spans 8 weeks. It is designed to cater to both undergraduate and postgraduate students, providing a comprehensive learning experience for participants at different academic levels
The course explores spatio-temporal statistics and data mining concepts. It then covers topics such as earth system observations, data analytics, and modelling in domains like hydrology, climate, and soil.
Participants will acquire practical skills in applying spatio-temporal statistics and data mining to analyse earth system observations. The course emphasises hands-on experience in data analytics and modelling.
Upon successful completion, participants will earn 2 credit points, enhancing their academic credentials. This recognition highlights the proficiency gained in applying statistical methodologies to address challenges within the earth system.