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Вернуться к Machine Learning Foundations: A Case Study Approach

Отзывы учащихся о курсе Machine Learning Foundations: A Case Study Approach от партнера Вашингтонский университет

Оценки: 13,082
Рецензии: 3,116

О курсе

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....

Лучшие рецензии


16 окт. 2016 г.

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much


18 авг. 2019 г.

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.

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126–150 из 3,043 отзывов о курсе Machine Learning Foundations: A Case Study Approach

автор: Ezra S

31 дек. 2018 г.

The only way these courses could be better if there were far more of them from the same professors. If more of the nitty gritty details of these algorithms were fleshed out in all their glory, more algorithms, more mathematical derivations & more tutorials in the programming languages & libraries used. Otherwise, these MOOCs are near perfection. A very, very nice introduction for beginners with just a little bit of math & not too much programming. Just enough for busy people. I've reserved that 5th star due to the slow pace that the MOOCs have been released (which will presumably be irrelevant for future machine learners) & the fact that there really needs to be more of these very high quality moocs. So there aren't enough of them, so I reserve a star. Hopefully in the future that will be irrelevant as well in which case I'll regret not indicating 5 stars.

автор: Ali O

22 февр. 2020 г.

This first part of the specialization course provides a gentle, well-balanced introduction to ML concepts. I had some partial knowledge about the subject and some familiarity with the statistical techniques, but the thing I needed most was some real-world requirements and applications. And I am very content with what is presented. The cases are concrete without being complex and while the course is careful not to drown you in theory, it does slowly build an insight into the techniques being used. Former Python experience is recommended, yet even if you have never used Python before, I think you can build the necessary amount of skill during the course. And I must not end without saying a couple of things about the instructors: They are amiable and entertaining as much as they are knowledgeable and that makes it a very pleasant experience.

автор: robert c

24 июня 2020 г.

Overall I thought it was a useful introduction to ML. I actually liked the fact that I needed to install WSL on my Windows laptop to run Turi Create. This provides me access to other programs and compilers that I usually access on Linux servers (e.g., gcc, gfortran, etc). I am fairly familiar with Python, but tend to use Spyder. Becoming more familiar with Jupyter has also been a bonus. One thing that I was not as familiar with was SFrames and the use of dictionaries within Python. I have mainly used Python for numerical linear algrebra, image processing, signal processing, and the like. Links to good tutorials for SFrames would be helpful. As an introductory course, it focuses more on applying techniques rather than specific details related to the techniques. Hopefully, these will be discussed more in-depth in the courses which follow.

автор: Ashishkumar P

2 мая 2020 г.

Very good course for an overview of Machine learning .

It is very good first step for Beginners. Word of caution : Course lecture videos uses the Graphlab. Course jupyter notebooks have the up-to-date Turicreaatecode. You can also use Panda/Numpty/Matplotlib to arrive to same conclusion. However I am new to ML/Python so stuck to course guided notebooks. it worked for me.

Course first gives you an intuitive and solid theory background and then walks you through how to implement that into Graphlab/turicreate.

Best part is Quiz / Assignment. You are not just copy pasting code from the class but really challenged to find a solution using turicreate API .

Overall good start. Recommend with foot note that be ready to do some googling on Turicreate while following along the course.

автор: M R

16 февр. 2016 г.

Overall, an excellent hands-on course to learn the basics of machine learning. I am really glad I chose this over many other options available online.

If I were to pick straws, a couple of the programming quizzes could be better, especially week 6 images quality and related questions 2 and 3, and couple of the theory quiz questions were misleading.

Finding all the iPython notebooks and data online on Amazon was a Godsend as those data files just refused to upload from my system. I wasn't aware that these were available so I could have saved 2 weeks of delay in completion due to data upload problems.

I am keen to continue my association with Univ of Washington and Coursera based on this experience though I am not in the market for certifications at this stage.

автор: Ram U

23 янв. 2016 г.

Great introduction to Machine Learning with the case study approach. Gives you a quick preview on what Machine Learning entails iwth practica use cases without making you learn a new language and several frameworks before you write your first line of useful code. The course seems to emphasize heavily of the practical uses of machine learning

The instructors have a pretty fun way of interacting with the viewers/students. Instead of reading power point slides. The quality of the content is great, though it can be made a bit more consistent in a few modules. The labs are pretty great and an important part of the course material.

The format of this specialization should serve as a template for future specializations made on Coursera.

автор: Brijesh P

12 февр. 2017 г.

The Course is well structured and gives a good overview of Machine learning and it's various applications. By working with case studies, it is easy to understand the logic behind the application. The only regret is that the usage of GraphLab means that it is a bit difficult to transfer the learning to something more open-source such as sci-kit. Graphlab does most of the algorithm work for you so you might not really understand the backstage working of it. Hopefully the courses further in the specialization will teach what is happening behind the scenes. Also, all the data is cleaned already so it is very easy to work with. you will have to learn data cleaning and data mining separately to be able to use it in your daily work.

автор: Niyas M

31 окт. 2015 г.

Brilliant course!

This course it taught by two amazing professors- Carlos Guestrin and Emily Fox. In the very first session itself, they make it crystal clear that they love what they do, and in the subsequent videos they show you why you should get excited as well.

The course puts remarkable emphasis on the concepts rather than raw code, and once you're prepped on the fundamentals, you're gently introduced into the code. This to me is a wonderful way to go about a new subject.

The slides are simple and clean, the work of a brilliant team shines through the entire time. Carlos and Emily are really funny and you just fall in love with this course because of these two exceptional teachers.

Without a doubt- highly recommended.

автор: Deleted A

28 янв. 2016 г.

Since when I got to know coursera, I have been going through plethora of machine learning courses .I couldn't complete even one of them as the pace was too boring and I couldn't connect anything to the real world scenarios. So I had paid a huge sum and joined a course on classification model using SAS near my place. To my astonishment, this course by the Amazon professors far excelled than what I took up earlier and I feel I had wasted money to pay that institute. This is the best machine learning course ever which has the theory and practical stuff at the same place. Thank you Amazon professors for all the help. Hope I start actively participating in kaggle competitions once I finish the other courses too.

автор: Iman K

25 дек. 2015 г.

Great introduction.

I have two suggestions:

1- GraphLab is a great tool but it is commercial and it is not available in all companies. Hope there were some guidelines, slides with sample source code etc. for each session showing how to complete assignments with an alternative free library.

2- In the slides, I found very useful practical hints mentioned quickly by the lecturers (e.g., how to fit a better regression model by using the same feature with higher order such as X^2, X^3, X^4...). There is a high chance to overlook such hints since they are mixed with other materials. It would be nice if such practical hints are gathered and published in a separate document like a cheat sheet for future references.

автор: Asma

30 дек. 2017 г.

The instructors are amazing and teach at a pace, which is slow enough for things you are new to but fast enough for more familiar things. I had decent machine learning background but none of SFrames. However, I had no difficulty understanding SFrames from the word go. The assignments are the best part. If new methods pop up in the programming exercises, step by steps instructions explain how to use them and you learn even more that way! (some of the hint/help links in the assignments did not work for me). Instructors used a lot of examples during lectures but I felt that theoretical part was lacking depth (may be it is me because I always like to see more maths :D). Carlos was great so were his shoes :D

автор: Benoit P

29 дек. 2016 г.

This whole specialization is an outstanding program: the instructors are entertaining, and they strike the right balance between theory and practice. Even though I consider myself quite literate in statistics and numerical optimization, I learned several new techniques that I was able to directly apply in various part of my job (ok, not in the foundations course, but in subsequent courses). We really go in depth: while other classes I've taken limit themselves to an inventory of available techniques, in this specialization I get to implement key techniques from scratch. Highly, highly recommended.

FYI: the Python level required is really minimal, and the total time commitment is around 4 hours per week.

автор: Andrea C

27 июля 2016 г.

This course was not very simple to understand for me, because i'm not a native english speaker and my english knowledge is academic; but thanks to english subtitles and transcriptions of videos i was able to keep pace with teachers as they talked and thanks also to the excellent learning material. The mathematical concepts explained are clear and concise, lessons are not lost in learning theoretical ramblings, but everything is set for the application of the acquired concepts in real life.

Python is very simple programming language to learn, but if you want to deploy a real machine learning application you must study other aspects of the language that, rightly, are not covered in this amazing course.

автор: Susanne E

10 окт. 2018 г.

This is a fun course that gives you a very good overview for different machine learning methods. It is indeed a case study approach which is very nice because you get an idea of how versatile machine learning and its application really is. Plus, you get to solve some meaningful questions using machine learning yourself. This first course doesn't explain how the algorithm works in detail but on a higher lever so that you understand the underlying idea and principle.

The videos are super fun to watch as Carlos and Emily are super likeable, and very engaged and excited about the things they're doing and teaching. Thank you so much, I had a great time doing this course!

автор: Edward F

25 июня 2017 г.

I took the 4 (formerly 6) courses that comprised this certification, so I'm going to provide the same review for all of them.

This course and the specialization are fantastic. The subject matter is very interesting, at least to me, and the professors are excellent, conveying what could be considered advanced material in a very down-to-Earth way. The tools they provide to examine the material are useful and they stretch you out just far enough.

My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning. I hope they get to do that some day.

автор: Phil B

19 янв. 2018 г.

The course gives an excellent overview of the main types of algorithms in Machine Learning. The lecturers are both very clear and I like the combination of annotated lecture slides and jupyter notebooks.

My only problems with the course were with the coding/assignments sections. Because of the choice to use GraphLab, I was forced to install a virtual environment with Python 2 to be able to run the jupyter notebooks myself. I understand the choice and agree that GraphLab is a very intuitive and easy to use module, however if it is not going to be updated for Python 3 then the coding sections should be re-recorded using a different library.

автор: Saransh A

14 сент. 2016 г.

This is one hell of a course

This is basically a good introduction to each and every basic ML technique, what makes this distinct from the other courses is that, you really get your hands dirty with writing code on Python, which is really an industry relevant language for Data Sciences and ML

The theory is also, taken in detail and the assignments are cleverly designed, this course was a joy to take. Initially, I thought that I would complete this course in a span of 6 weeks, which is actually prescribed. But, the fluidity of the course content was so great, ended up completing the course within a week

Definitely, taking the advanced courses

автор: Stefano P

16 нояб. 2015 г.

I appreciate this course that is one of the best mooc courses I ever done.

Being a computer science engineer, I found the "hands on" approach particularly amazing, it lets you immediately start applying the topics with real use cases.

I started this course after completing the Machine Learning course of Andrew NG (Stanford University), and this made a perfect companion.

I think that if I didn't have followed the NG course, probably I would miss something from the theory, particularly maths and statistic that in this course are missing. (in any case where superbly covered by NG course)

Thanks a lot for the course

Stefano Priola

Turin Italy

автор: Nirav P

22 сент. 2016 г.

Excellent Introduction to the Machine Learning applications. This is course does not cover all the "details" of the method, but rather just prepare/motivates the use machine learning. Only criticism would be that course relies on GraphLab Create, but frankly speaking this is quite alright as I mentioned that the point of the course is to deliver the motivation and show some interesting applications of machine learning rather than explaining every detail of each module. If you want more details, take the entire specialization where you have more freedom to write your own code without the use of the use of Graphlab.

автор: Yamin A

30 дек. 2018 г.

Excellent introductory course on Machine Learning. The material is taught at a level that does not require much in terms of pre-requisites, both in terms of the math and the programming requirements. From my perspective, I have an extensive background in Math, and some background in programming (MATLAB, R). I had not used Python prior to this course, and I found that I could keep up and learn both some Python and ML. I was able to finish the course in two weeks. Well done to the instructors who made the videos fun and accessible. Recommended for anyone who wants to learn something about ML.

автор: Rohail K

9 янв. 2018 г.

Amazing course on Machine Learning.I have tried other courses on Machine Learning but none has made it so simple for me as this course.I started other courses but at some point I was stuck but this course explains all concepts so easily and gradually .Highly recommended for anyone who want to start learning machine learning.Even if you do not have programming experience, its easy to follow.

I congratulate both the instructors Emily and Carlos for making this brilliant course.

My most favorite part of this course is when Emily is trying to pronounce the name "Pele" and Carlos corrects here lol.

автор: Siva J

22 февр. 2016 г.

A very well designed course. This course clearly sets a path for introducing basics and fundamentals in a way that makes it challenging but insightful. It establishes the purpose for more advanced courses in this specialization and the need to dive deep in this fascinating subject (machine learning).

After completing this course alone, I can say categorically, there is no turning back, there is no sitting on the sidelines, there is no learning something peripheral. Doing this entire specialization will make any successful learner a very successful contributor in the world of Machine Learning

автор: Yanan L

1 июня 2016 г.

A little touch on the basic concepts and a detailed step by step tutorial on applying ML using python packages, this could serve as a good complement to the beloved Stanford ML course on Coursera. For someone who is just getting started with Python, I think this course would be significantly more useful if the packages used are pandas and scikit-learn, since those are more heavily adopted in the industry. The course is well organized and the programming examples are fun. This is a great introduction course to ML and I do expect highly of the subsequent courses in this specialization.

автор: Arif A

24 мар. 2019 г.

I have a fairly good background in mathematics and have read through major parts of the Deep Learning Textbook by Goodfellow et. al. One year later I wanted to revise ML again. People who are complaining that there is no mathematical or algorithmic rigor in this course need to understand that this is meant to be an introductory course in order to pique interest in the learner and drive him/her to pursue this field further. Heavy focus on math and algorithms straightaway does not work for most people. Hence, I conclude that this is a good intro course which does it's job quite well.

автор: Kowndinya V

4 мар. 2018 г.

This course provide a very intuitive explanation of different machine learning models. It also has a good blend of hands on programming. Especially the combination of python notebook and graphlab give a unique experience. The visualizations from graphlab are amazingly good. It's really additive.

I would like to Thank Emily and Carlson for their great work in putting the right level of content for this course keeping the audience in mind. I feel bottom of my heart that I could really learn something significant and meaningful.

Overall, I must say it was an awesome experience.