Coursera releases updated Machine Learning Specialization course

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

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Online learning platform Coursera recently announced the launch of its new Machine Learning Specialization course. This beginner-level program teaches the fundamentals of machine learning and how to use these techniques to build real-world AI applications. The class was developed as a collaboration between DeepLearning.AI and Stanford University. Taught by AI visionary Andrew Ng, the Machine Learning Specialization course is an updated introductory program, expanded from 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 enables the global workforce to learn and deploy AI systems. Ng is also general partner at AI Fund, an adjunct professor at Stanford University’s Computer Science Department, and chairman and co-founder of Coursera. He was previously the founding lead of the Google Brain team and chief scientist at Baidu. He is the author or co-author of over 100 research papers on machine learning, robotics and other AI-related subjects.

The updated Specialization courses examines advances in machine learning during the decade since Ng created the original course. Machine Learning Specialization comprises three comprehensive modules introducing machine learning, supervised learning and unsupervised learning. Lectures and graded assignments teach Python rather than Octave or MATLAB. Code notebooks and interactive graphs assist learners in understanding the concepts presented in the class.

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

Learners who complete the program will master key concepts and gain practical knowledge that will allow them to apply machine learning rapidly to challenging real-world problems. The new Machine Learning Specialization is the ideal starting place for those who would like to break into AI or build a career in machine learning.

The Machine Learning Specialization course should take approximately two months to complete, with a workload of about eight hours a week. While the course is geared toward beginners, participants will need to understand basic coding and high school-level math.

In his announcement video, Ng explained that he designed the new program to minimize the amount of math necessary.

Upon completion of the program, learners will receive a certificate that they can share with prospective employers and professional networks.

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