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Andrew Ng announces a new ML specialisation on Coursera
Andrew Ng announces a new ML specialisation on Coursera

Andrew Ng announces a new ML specialisation on Coursera

The 3-course program is a new version of Ng’s pioneering machine learning course, taken by over 4.8 million learners since 2012.

Published Updated

Key takeaways

  • Andrew Ng's new Machine Learning Specialisation on Coursera is designed for beginners to learn essential machine learning concepts.
  • The programme includes three courses, covering supervised and unsupervised learning techniques and AI application development.
  • Since its launch in 2012, Ng's original course has attracted over 4.8 million learners worldwide.
  • Learners will gain practical skills in Python using libraries like NumPy and scikit-learn for building machine learning models.
  • The course also emphasises best practices for ensuring the effectiveness of machine learning models in real-world scenarios.
5 min

Andrew Ng’s DeepLearning.AI, in partnership with Stanford Online, recently announced a new Machine Learning Specialisation course on Coursera. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 

The 3-course program is a new version of Ng’s pioneering machine learning course, taken by over 4.8 million learners since 2012. 

The program provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation.

https://twitter.com/AndrewYNg/status/1537119911889145858

The first course teaches you how to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression

The second course teaches you how to:

• Build and train a neural network with TensorFlow to perform multi-class classification

• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world

• Build and use decision trees and tree ensemble methods, including random forests and boosted trees

The third course teaches you how to:

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.

• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.

• Build a deep reinforcement learning model.Click here to enrol for the course.

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