By far THE best course out of the standard Data Analytics course and Advanced version. The lecturer is passionate about what she is delivering and seems to be a master of decomposing a concept into bite-sized chunks which makes learning an already fascinating topic even more enjoyable.

To add, there's no waffling here, examples are fine-tuned to the scenario and easy to go along with, and I've noticed that each course is structured differently depending on the person who created it (I'm assuming it's the lecturer/presenter) - but, whoever it is, they have done a good job scaffolding this course effectively.

My take on the quality of courses within the Advanced version thus far is that all of them are good quality but Course 5 (Regression Modelling) needs a lot of work to even be considered to be on this or Course 4's (Statistics) level.

The course was OK and it wasn't too much in detail I had to first read the book hands on machine learning and then watch lectures to get a good understanding however the labs were great

The material is highly informational and the examples provided are exceptional. The follow-along guides have been my favorite part of this specialization because I understand topics much easier when they are presented in Python since my background is in programming. The end-of-course project was the most thorough so far and even included test comparisons from two different models. I feel much more comfortable now pursuing the subject further in other courses now that I have a good foundation of the basics.

Theoretical concepts are usually presented in a simple and understandable way. However, when it comes to practical tasks, things that were never discussed in the videos start appearing. These steps are just done, and you’re left wondering how and why they are being done. So there is a gap between theory and practice that can make the learning experience confusing and frustrating.

The materials and presentations are excellent, but I am not a fan of the busy work involved in the end of course PACE portfolio work. Professional presentation is certainly important, but you end up diluting the course by including so much of this material. Furthermore, having worked on many projects, I can tell you that the workflow process is so iterative that trying to "shoe horn" the process into a four letter acronym -- which is better than the six letter acronym in the previous specialization -- is both reductionistic and frankly artificial. My suggestions are as follows: remove this excess material and "re-factor" the PACE/portfolio material into one course, perhaps the capstone course. Otherwise, I learned quite a bit.

Very nice program covering decision trees, random forest, bagging, boosting, and XG boost models. Helped strengthen python skills. Worth the time investment.

Great course. My favorite course from both the Google Data Analytics & Google Advanced Data Analytics courses.

Thanks for such a wonderful course.

Great intro to machine learning. The instructor did well in breaking down the concepts and explaining the python code.

The course is fine , It can be reviewed by two main points: 1. It needs further explanation covering some concepts, I didn't understand that much 2. The Labs are good

This course, The Nuts and Bolts of Machine Learning, has gone in great length to explain how a machine learning model works to solve a business problem. This course covered different types of classification techniques, machine learning models and few other important topics related to machine learning. The instructor of this course, Ms. Susheela, explained covered topics by providing good details and explaining these topics clearly. Also, another instructor who appeared at the end of course also provided good tips on portfolio projects which have been completed through out the course and how to discuss them to a hiring manager when interviewing for a data analytics/ data science employment opportunity. Thanks to Coursera platform for hosting this worderful course and to Google team who created this one of the most important course as part of the Google Advanced Data Analytics Professional Certificate program.

Upon successfully completing "The Nuts and Bolts of Machine Learning" on Coursera, I am elated to convey my utmost satisfaction with this extraordinary course. The meticulously curated content showcased a thoughtful organization, imparting a profound understanding of the core principles of machine learning. The instructors' unparalleled expertise and lucid explanations rendered intricate concepts easily accessible and utterly captivating. The hands-on exercises and real-world examples proved pivotal in reinforcing my learning, elevating my proficiency to confidently employ machine learning techniques. The peer-graded assignments fostered a strong sense of community, enabling invaluable feedback exchanges among learners. Overall, this course has been an invaluable learning odyssey, furnishing me with indispensable tools and knowledge to embark on thrilling machine learning endeavors with unwavering confidence.

Course 6, 'The Nuts and Bolts of Machine Learning,' in the Advanced Data Analytics specialization by Google on Coursera has been an invaluable learning experience. The course effectively covered the different types of machine learning, the workflow for building complex models, unsupervised learning techniques, and tree-based supervised learning. The end-of-course project provided a practical opportunity to apply the newly acquired knowledge. I deeply appreciate the clear explanations, real-world examples, and hands-on exercises that made the learning experience both informative and engaging. This course has significantly enhanced my understanding of machine learning and its applications in data analysis. I highly recommend this course to anyone interested in gaining a comprehensive understanding of the fundamentals of machine learning in the context of data analytics.

This is a very interesting course which tackle the challenge of trying to explain machine learning to students with (in my case) a very limited knowledge of it. It is very well explained and the frequent reminders of what we've learned previously is very helpful not to feel overwhelmed by the density of the lessons. the python labs require more attention than previously but it really pays off and it's truly enjoyable to reach the end and be able to create a machine learning model. Challenging but extremely rewarding. I couldn't have hoped for a better course.

This course is great but I took it by mistake a bit too early for myself and it was tough. I would have learned much more if I took this course in the right sequence. I would have to revisit this course in the future. Great projects and examples!

A great course for anyone who wants to dive into the world of Machine Learning. The steps are easy to follow and the lectures and lengthy enough to give a complete idea of the topic.

The most fun part of the certificate. Building models is the meat of program, and you can begin playing with your own datasets after this section.

Being an experienced programmer with math background but with rather outdated knowledge of ML, in order to refresh and expand my knowledge, I decided to take a series of ML courses from Andrew Ng, and then "The Nuts and Bolts..." (TN&B). TN&B pleasantly surprised me with an abundance of theory combined with a developer's perspective. Some topics, especially K-means, in my opinion, are explained even better than in the course from Andrew Ng. The labs and activities in Python seemed interesting to me at first, but by the end they were reduced to the monotonous creation of Tree Forests or XGBs. The final project was quite boring and, frankly, by the end I could not stand it and wrote part of the code using lots of copy-paste techniques. It seems to me that the authors of the course should have paid more attention to the issues of analyzing various scores and confusion matrices, since these are rather sophisticated topics. Based on the results, I can say that after the course there is practically no chance of becoming an ML programmer or getting a corresponding job if you were counting only on this course or specialization. But if you already have programming experience and some basic knowledge of ML, then the course is well suited to update your knowledge and get the big picture.

A good introductory level course on some common ML models. It does teach students with little work experience how to communicate with fellow colleagues and stakeholders.

very less visuals and much of oral teaching for these type of topics, infact youtube channels such as 'StatQuest with Josh Starmer' have the outstanding content for learning such topics! Am very upset with it and the only thing i like in previous two courses is the lab otherwise the method of teaching is not good.

If you are a beginner and you wanna have a good foundation on Machine learning especially from Data Analysis point of view, I think this is the best place to start off as you put all your learning skills at the end of the course's data analytics project , which is a very thoughtful step by google to make their potentials learners equip their resumes with great critical thinking projects. Thanks to Google !!