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In this course, the instructor from his experience gained through several machine learning and deep learning projects explains how to prioritize tasks in a big machine learning projects. This course does not introduce the reader to CNN or RNN but rather makes the user aware of some ML/DL tips to make the most efficient use of time and resources. Some of the most important questions addressed in this course are: 1) Why a single evaluation metric is important and what are some of the widely used metrics? 2) What is human-level performance and is it a good estimate of Bayes error? 3) What is Orthogonalization in the context of ML tasks and why is it important? 4) How to measure avoidable bias, variance error, data mismatch etc? 5) How to address data mismatch error? What is transfer learning and how is it different from multi-tasking 6) Whether one should opt for traditional or end-to-end deep learning approach?

This course from setting machine learning strategies, setting goals, error analysis and data distribution, migration and multitasking learning, and depth of the end-to-end neural network training and so on about the strategy of machine learning, to strengthen the depth of the first two lessons we learn the basic knowledge have the very big help, deep understanding of the depth of these knowledge is very good for our study harder to learn knowledge, such as convolution neural network. The greatest help of this course is that it makes us understand how to solve problems encountered in the actual development process and what is the most reasonable solution through two case studies.

In this course, Andrew is giving very interesting practical insights into how to proceed in different project settings and how to speed up each iteration. Think of it as a stand-alone optimization algorithm for deep learning projects. What I'd further expect from this course are practical assignments, e.g., data acquisition and preprocessing patterns, data (image) augmentation, and transfer learning and multi-task learning (preferably building upon introduction to tensorflow in the previous course). As I already stated in the previous previews, optional assignments without grading would also do the work in motivating the students to do something on their own.

Great insights as usual for these courses. Especially useful are the strategic insights for dealing with data mismatch between train and dev/test data sets; my favorite is the idea of a "train-dev" set to separate variance from the differences in data distributions, which had never occurred to me despite it being obvious in hindsight. The "flight sim" tests were more challenging than I expected, and really helped to cement the concepts into memory. The only criticism is that some coding assignments would have been helpful to put these ideas into practice in a guided manner. Otherwise, great course as I have come to expect from Andrew.

This course is more about explaining how to set your analysis universe(train/dev sets etc.) and where to go when u hit a road block i.e. when to concentrate on bias/variance etc.

Suggestions: Unlike other courses, no programming assignments here .. may be some programming assignments + Quiz in a case study format would have been more helpful. E.g. present a case, ask the student to write piece of code to calculate bias and other metrics, and then ask questions from the metrics derived instead of mentioning directly the values for human level error, Bayes estimate.

This is a great course, something I will keep coming back to even after I'm done because it talks about strategy and rules of thumb re: Machine Learning/ Deep Learning approaches. It introduced me to certain concepts that were brand new for me and that was a great outcome for me. I wish the audio was better and the notes were better because writing on the small screen really hinders expressibility. I would rather have Dr. Ng write/draw on a chalk board than the small screen, I feel it really constrains his process. Still it's a great course!