DS
Hands on practices are very good. learning predictive model was a challenge.
Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection
DS
Hands on practices are very good. learning predictive model was a challenge.
KR
Very nice assignments and content. You learn a lot when you complete all assignments.
NE
I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.
KP
I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .
GJ
This course helpemd me understand more about machine learning and a set of tools to help with the same.
RS
Very good approach to each method; the assignments are a good test for the topics.
FY
Its Hard! but AWESOME, some much info packed in a few lectures!
TR
Its a great review course. Prior knowledge is necessary
HD
The entire course is an overview! This course will be a revision if you already know the concepts.
PV
The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course
CY
Nive that the course covered a broad range of topics.And good to get pushed to do some kaggle competition and peer review.
AS
Excellent course with amazing practical exercises!
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The lessons are sometimes completely disconected from the graded assignments. There were some graded assignements that dealt with things I have never heard about and I completed it without even looking the lessons videos. Some of the lessons are disapointing of the lack of assistance to the required software/code to be used. In such a way that the concept worked is very simple, but if you have no experience on the software or code you can have a hard time to complete the assignements with irritating details which are not explained at all in the lessons. The lessons serves more as a guide to what you should search in google and learn through other source of information. I did not expected such poor course from a paid one; I have doen free courses way better than this course. Don´t pay or this course, find some other course free or other paid course with better reviews.
Do not like the slides and the way it is explained. Compared with other ML courses on cousera, this one makes me feel that it is more like a handbook/dictionary instead of a tutorial to teach students. If you already know it, it would help you refresh the mind. Otherwise, you might find it is just to show off how how complex and mysterious is the data science.
I can feel Prof. Howe tried to cover as much as possible and to build a foundation for both practicing as well as further study on the topics. However, I do feel it is not patient enough to give a detailed yet easy-to-follow explanation for some of the topics, and I had to do quite some self-readings to close the gap. I think it will be helpful if the course can provide some reading materials on how some of the formulas are derived (e.g. gradient descent, logistic regression etc.) as a supplement.
Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.
I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .
The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course
Nive that the course covered a broad range of topics.
And good to get pushed to do some kaggle competition and peer review.
A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.
This course helpemd me understand more about machine learning and a set of tools to help with the same.
Too little people participated and long peer review time.
But the course content is good.
Very nice assignments and content. You learn a lot when you complete all assignments.
Professor Bill Howe gives great reactions to when there are typos on the slides!
Hands on practices are very good. learning predictive model was a challenge.
Its Hard! but AWESOME, some much info packed in a few lectures!
Its a great review course. Prior knowledge is necessary
Excellent course with amazing practical exercises!
Excellent thoughts and concepts presented.
Excellent course
great for learner
Excellent Course