It's quite important to highlight that what was Ng's single course is now a specialization containing 3 courses.

The site describes the specialization this way:

It 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 (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

A question:

From the email -

An expanded list of topics that focus on the most important machine learning concepts (such as modern deep learning algorithms, and decision trees)

How much deep learning is in the original course? I got the impression - perhaps wrongly - that it was mostly about older approaches.

Does it make sense for a learner to jump directly into deep learning?

(Obviously the quote above doesn't say "directly into". And "Deep learning" isn't mentioned in the course overviews until Course 3, "Unsupervised Learning, Recommenders, Reinforcement Learning”… But I’m still curious.)