I’m part of the analytics platform development and before that worked on several data solutions for internal company use so when I noticed Andrew Ng’s announcement about the brand new AI course, I thought it would be great opportunity to structure my knowledge about the machine learning and specifically its deep learning variant.

If you wish to verify your own technical understanding of the topic, check out how comfortable you are with this deep learning emulator.

If you are fluent with tuning all the parameters (you know exactly what you’re doing and why) then you can probably jump directly to Course 3: Structuring Machine Learning Projects while still checking Course 2: Improving Deep Neural Networks for the techniques you are not familiar with or you want to improve. Otherwise, start from Course 1: Neural Networks and Deep Learning.

Generally I highly recommend this Specialization for:

Engineers and specialists being part of cross-functional analytics team involving software development, data science and domain expertise

Product owners (or product managers) who want to take the data product to the next level but avoid the widespread AI hype and false expectations that may come from it

Leaders building and developing the analytics team/unit

AI enthusiasts who are not sure if and how the deep learning can offer real value to achieve the business goals

As its title and the outline suggests, this is not about:

Not a high level overview or BI training (be prepared for heavy topics)

Not a walk-through of particular machine learning framework (however in the last programming exercise the Tensor Flow is introduced for the sake of presenting the benefits of the frameworks)

Not a deep dive to big data software stack (e.g. Apache Spark)

You can access deeplearning.ai or go directly to coursera.org. At the time of writing this post, there are three out of five courses available. Below few things which make this course unique in my opinion.​

Almost no prerequisite

The course outline mentions the prerequisites such as basic programming experience, linear algebra and basic understanding of machine learning purpose. Even if you haven’t touched any code for very long time but you once coded anything and you recall for loops, arrays, vectors and data types, you will not have any problem.

As for the linear algebra, first few weeks refresh the definition and application of derivatives, loss functions, etc. No aspect or nuance is left unclear.

My favorite nuance; Andrew Ng explains a saddle point :)

Machine learning flight simulators

A novel approach to walk you through a real-life project and the associated problems. It is a series of what-if questions and decision points testing your knowledge about the methodologies and the best practices.

I consider this part especially valuable for the leaders. It gives enough insight on the project methodologies so that you can effectively help your team/unit deal with their impediment backlog. Aside from explaining the performance optimization techniques, Andrew Ng spends significant time discussing how you can better structure a project depending on the amount of data at hand.

Heroes of Deep Learning

This is the optional content in which Andrew Ng interviews the elite researchers and gurus of the deep learning world. Google the names and you realize how much of their research has been productized in every day applications.

If you need some motivation to start the training, I recommend this story from Google Translator. You’ll read about the involvement of Andrew Ng as well as some other individuals interviewed in Deep Learning courseware.

Zero-overhead programming assignments

In contrary to many other courses, this one will not require you to install anything locally or create any cloud account. You get access to one of the most popular data science labs, Jupyter Notebooks, right from the Coursera platform. Everything in Python so within reach for the majority of engineers as well as those not actively coding but with the basic programming background.

You are graded against the course content, i.e. no data parsing, all auxiliary code is clearly commented, works without a problem and lets you realize how much work there is beyond building the deep learning model.

Quality + pace = motivation

This is top class quality. The pace and the amount of information per training module is just right making it perfect to allocate 45 minutes uninterrupted slot. The programming exercises are well balanced keeping your motivated at the highest level all the time. If you are a cat fan, this course is a must as you start with a cat image recognition model.

Cat and non-cat pictures taken around the world, pushed through my cat recognition model

Strong brand

Andrew Ng is one of the most respected machine learning experts gurus. No doubt that having his course certificate will be a solid point in your personal development plan.

Your recommendations?

Feel free to share your recommendations in case you develop your own or your team’s machine learning skill set.

EDIT: The Coursera Data Science Community is a good place to get more recommendations, discuss the projects and ideas, find peers for discussion.