Logo of online learning platform Coursera displayed on tablet screen. Editorial 3d rendering.

Coursera Releases Updated Machine Learning Specialization Course

The new online learning platform course teaches the fundamentals of ML with less emphasis on math.

Logo of online learning platform Coursera displayed on tablet screen.  Editorial 3D rendering.
Image: HT Ganzo/Adobe Stock

Online learning platform Coursera recently announced the launch of its new Specialization Machine Learning Class. This entry-level program teaches the fundamentals of machine learning and how to use these techniques to build real AI applications. The classroom was developed as a collaboration between DeepLearning.AI and Stanford University† The Machine Learning Specialization course, taught by AI visionary Andrew Ng, is an updated introductory program, an extension of his original Machine Learning course, which was taken by nearly five million people.

Andrew Ng is the founder and CEO of DeepLearning.AI, an education technology company that empowers the global workforce to learn and deploy AI systems. Ng is also a general partner at AI Fund, an adjunct professor in Stanford University’s Computer Science Department, and chairman and co-founder of Coursera. He was previously the founder of the Google Brain team and chief scientist at Baidu. He is the author or co-author of more than 100 research papers on machine learning, robotics and other AI-related topics.

The updated specialization courses examine advances in machine learning over the decade since Ng created the original course. Machine Learning Specialization consists of three comprehensive modules that introduce machine learning, supervised learning and unsupervised learning. Lectures and graded assignments teach Python instead of Octave or MATLAB. Code notebooks and interactive charts help students understand the concepts presented in the classroom.

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In the Machine Learning Specialization course, students will build ML models with NumPy and scikit-learn, develop and train supervised models for prediction and binary classification tasks, build a neural network and train with TensorFlow to perform multiclass classification, make decisions trees and tree ensemble methods to adopt and use, apply best practices for machine learning development, and more.

Students completing the program will master key concepts and gain practical knowledge that will allow them to quickly apply machine learning to challenging real-world problems. The new Machine Learning specialization is the ideal starting point for those looking to break into AI or build a career in machine learning.

The Machine Learning Specialization course lasts approximately two months, with a workload of approximately eight hours per week. Although the course is aimed at beginners, participants should understand basic coding and high school math.

In his announcement videoNg explained that he designed the new program to minimize the amount of math required.

Upon completion of the program, students receive a certificate to share with potential employers and professional networks.

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