Mathematics for machine learning from Imperial college London
Imperial College London in partnership with Coursera offers Mathematics For Machine Learning Specialization (View here). The best part is, anyone from any part of the world can enroll in this specialization. There is no need to attend any classroom, everything is 100% online!
Anyone who wants to master Machine learning and Data Science must consider this Specialization which consists of 3 courses. We discuss each course in this article down below, make sure to check that out!
Note: You have a 7 days of free trial with all coursera specializations! If you decide to pay in advance, you get 10% of discount!
Courses in this Imperial College Specialization
As mentioned above, mathematics for machine learning specialization consists of three courses. Though, each course is essential, you can still decide to do all of them or take one course as you require. Following are the courses and the content they offer to the learners:
- Mathematics for Machine Learning: Linear Algebra – Total 27 videos, 4 readings and 14 quizzes are there in this course. You can easily finish the course in 4 weeks if you go by the schedule. Otherwise, this approximately can be finished in just 21 hours 🙂
- Mathematics for Machine Learning: Multivariate Calculus – This course has 34 hours of video content, 4 readings and 19 quizzes. The course is covered in 4 weeks, you can still finish it anytime according to your schedules! The entire length of the course is about 22 hours
- Mathematics for Machine Learning: Principal Components Analysis (PCA) – This is the last course, you get 32 videos, 13 readings and 14 quizzes in the course. Again, this is also a 4 weeks course, learners can complete it according to their schedules! Total length of this course is 18 hours
These courses start from a beginners perspective, anyone can enroll and learn from them!
Instructors of mathematics for machine learning
This top class specialization from imperial college London in partnership with Coursera is the product of these incredible instructors:
- David Dye, Professor of Metallurgy Department of Materials
- Samuel J. Cooper, Lecturer, Dyson School of Design Engineering
- A. Freddie Page, Strategic Teaching Fellow, Dyson School of Design Engineering
- Marc P. Deisenroth, Lecturer in Statistical Machine Learning, Department of Computing
Consider learning from these instructors and do yourself a great favor! Don’t forget to let your friends know about this specialization if they also have interest in learning Machine Learning.
Machine learning course by Stanford University
If you want to go deep into machine learning and advanced stuff, you must take this specialization. This is more of a prerequisite, so, go ahead and enroll today! We highly recommend it to all our readers interested in learning machine learning.