>The Machine Learning Specialization is designed to be accessible for first-time learners and includes:
>An expanded list of topics that focus on the most important machine learning concepts (such as modern deep learning algorithms, and decision trees) and tools (such as TensorFlow)
>Assignments and lectures built using Python -- the programming language of choice for machine learning developers
>New ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is doing and make it easier to complete programming exercises
>A practical advice section on applying machine learning which has been updated significantly based on emerging best practices from the last decade
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.)
