Chevron Left
Retour à Machine Learning Foundations: A Case Study Approach

Avis et commentaires pour d'étudiants pour Machine Learning Foundations: A Case Study Approach par Université de Washington

4.6
étoiles
13,204 évaluations

À propos du cours

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....

Meilleurs avis

PM

18 août 2019

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.

BL

16 oct. 2016

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

Filtrer par :

126 - 150 sur 3,063 Avis pour Machine Learning Foundations: A Case Study Approach

par Christos P

2 nov. 2022

WHY YOU MAKE THIS SO HARD TO US TO INSTALL THE PACKAGES AND THE LIBRARIES???

I HAVE TO UNENROLL THE COURSE BECAUSE I COULDNT FOLLOW THE INSTRUCTIONS OF HOW TO DOWNLOAD THE STUPID TURI. IN ALL OTHER COURSES THEY SAY DOWNLOAD THIS FILES AND USE THEM ... BUT NOOOO YOU HAD TO MAKE IT SO DIFFICULT .. WE HAVE TO USE CMD AND MAKE VIRTUAL MACHINE AND DOWNLOAD FROM THERE AND THEN DOWNLOAD JUPITER AND INSTAL AND YOU DONT EVEN GIVE THE RIGHT INSTRUCTIONS... I HAD TO SEARCH TO GOOGLE YOUTUBE ANS STILL YOUR STUPID PACKAGES ARE A PAIN IN THE A$$

par Sara K

29 sept. 2021

The teachers are stiff and, although they say the course involves a case study approach, you start with the basic Python assignments that will not apply to the cases. Also, they push a tech that does not seem to be the most commonly used one in Machine Learning. I wish Coursera would put a date on these courses so we can see when the courses are older, and therefore expect the content to be outdated.

par Nethmini K

21 juil. 2022

Plenty of issues with this course. The main issue is with the assignments. The instructions use graphLab but we have to use turicreate and sometimes this gets confusing.

The material is outdated.

The assignments do not have a clear structure. Too much jargon. A clear step-wise approach would have been better.

Disappointed overall.

par Todd W

21 déc. 2021

The tools needed for the course were impossible or nearly impossible) to get set up. The discussion forums were full of users struggling and others offering very complex and ways to try, yet none of them seemed to work. I spent about 5 hours failing to install the tools and finally quit the class.

par Spiro D

18 nov. 2021

i wish i read the reviews before starting this course... it is well explained but the fact that i am unable to load a dataframe due to being on windows is mindboggeling and greedy from the creators of the course (that happen to be the creators of this package). just aweful

par Sreekanth K J

9 juin 2021

Please mention that users need to use turicreate and sframes etc "only" to complete the assignments.

it is wrongly mentioned that one can complete assignments using any tool but very first assignment is forcing us to use turicreate and sFrames !!

par Santosh K

14 nov. 2021

Uses Outdated graphlab. Very difficult to follow the instructions. Tried using the turicreate but its very problematic. The courses should ease of use tools rather than something outdated.

par Sahil K

24 nov. 2020

Software installation was a big trouble and took nearly month as this course was in graphlab, but we need to use turicreate or other toolset.

par Harry W

6 janv. 2022

You'll spend more time trying to get the correct versions and plugins working than you will studying. The course needs updating.

par Konstantinos V

3 nov. 2021

Started this course with great enthusiasm but end up with frustration on being unable to install Turi Create with python 3.10.

par Aaron B

29 juin 2022

This course is out of date and really useless at this point. Do not waste your time. None of the code or examples work.

par Muhammad A A J

12 juil. 2021

This course is very old and waste of time because libraries used in here are not available for new versions of python.

par MARC G

30 nov. 2022

Impossible to activate turi create on windows. it's a waste of time to discover during the course.

par Bowen S

25 nov. 2021

poorly structed with questions & answers

the packages used for the course has been out dated

par Kailash H S N

25 août 2021

very bad , theyuse SF frames which is not in use now ..very hard to do the quiz

par Pratick B

8 août 2021

Installation of Sforce and turi was not shown adequately enough.

par Maria C R B V F

17 sept. 2022

This course is outdated.

par Batuhan İ

9 août 2021

too old documents

par Ryan C

22 août 2016

This course is excellent for anybody new to machine learning and wanting to learn this new skill from the top down. For me, I have a strong background in machine learning, not in the context of big data, but I wanted to get familiar with Python and learn how modern companies are using machine learning in practice. This course provides that applied approach to implementing a broad range of machine learning applications with Python, applied to real problems.

A course this small cannot provide everything - what this course does not provide is in-depth technical tutorials on the workings of machine learning algorithms. There are many courses out there which do, but this course to great for learning a practical approach to problem solving with machine learning and data processing.

If there is a downside, I would say that the use of paid packages in the lectures (graphlab) limits the student's ability to learn Python using the freely available packages on the web, which was my personal preference. However, this is not purely negative, since there are many employers out there who would like to know that you have practical knowledge of things like AWS and graphlab. I did enjoy learning about those packages and services and I feel like I learned something positive which I can share with potential employers.

Overall, a very good concise course - one of the best on Coursera for vocational learning in my opinion.

par Tim J

9 janv. 2016

Excellent overview course. It has exactly the right balance between explaining Machine Learning concepts, and providing enough supporting mathematics & logic to understand why these concepts are correct (without going through epsilon-delta proofs).

Having followed several Machine Learning courses, this is now definitely my favourite new course, replacing Andrew Ng's famous course here on Coursera (which was also very good & especially complete, but required too often a leap of faith - this course provides really more details on the "why"). Furthermore, the exercises in this course are spot-on: they use Python and GraphLab Create (for which you get a 1 year student license when taking this course) - the big advantage is that you can focus on the Machine Learning aspect, and not on how to implement something in Python (or Matlab or R). The exercises are challenging enough and require some thought, exactly what they should do. This is not a "look up the right answer in the slides" course when it comes to exercises, which I particularly like.

The chemistry between the teachers is also very nice and shows they just love Machine Learning, and love teaching it (which they do very well).

If you some familiarity with statistics (a bit) and mathematics (a bit of matrix & vector calculations), and want to understand what Machine Learning is about, then this is THE course for you.

par Milan R K

19 févr. 2016

Emily and Carlos, you are the best! Thank you so much for offering this great course. I like your humor, your casual, yet very direct and practical, approach of teaching.

I'm a film student from Germany but I was always interested in Machine Learning and AI - more like a hobby. This course gave me a very good intermediate understanding for the mechanisms behind this hyped and often overcomplicated subject field. The knowledge I gained helped me deliver a way better master theses in film school. I was able to (automatically) collected huge amount of tv-series data on several platforms via import.io and dbpedia and build a really great, combined database (dato's SFrame was very helpful here!). Through the techniques of this course I was able to push the analysis in my thesis a lot further than I ever expected!!! I will try to finish the other courses of this specialization although I'm an expert and professional in a completely different field. It's just so much fun and so comprehensible!

Also I got the impulse for a great sci-fi television series, which I will be writing the next few months now ;)

par Cheng M H

14 mai 2019

I came into this course knowing little bout Machine Learning. In fact, besides knowing a touch of HTML, I have no significant background in computer programming. Even before I started watching the first video, I was already expecting this to be an especially challenging course, for me at least. However, I was pleasantly surprised with the content and delivery - Carlos' and Emily's adorably dorky banter and their clear and concise approach to the various case studies made it easy for me to grasp the fundamentals of Machine Learning. Their delivery of the course's content is beyond reproach. (Although I would have loved to see Carlos going on a little more about Messi and soccer in general!). I struggled a little on the last question of the final assignment (Week 6), but besides that, it was smooth sailing. Overall, it was a positive learning experience and I'm happy to say that I now know more about Machine Learning than when I began. If you're new to it, this course is a great way to learn what Machine Learning has to offer.

par Neil J

30 juil. 2016

Excellent content, and at just the right level for a getting-your-feet-wet-course. I especially liked the overall vibe of the lectures, which was relaxed and kind of goofy, and it's actually kind of nice to get some sense of personality from both Carlos and Emily. This is a topic of how to understand and manipulate the world as expressed to you through data -- a completely dry and theoretical approach would be tragic. I eagerly look forward to the rest of the specialization. And I had an ah-ha! moment in the week 5 homework -- it's a fairly simple model of building song recommendations, but when you actually look at the recommendations that come back from this algorithm, you kind of see that it does an intuitively better job than any system you could design and build without using ML techniques. Being a (successful) software engineer, this was both humbling to me and inestimably cool! It's not just a few new tricks to add to my bag-o-tricks, it's a whole new field to digest and investigate.

I'm very excited about this!

par Patrick M

1 févr. 2016

A fascinating tour of what's possible today with modern machine learning tools. The beauty and challenge of this course is the approach - diving right in to the tools to work through and experiment with some case studies. This is not a talk and visuals only course. You will be hands on.

This may be demanding for some, but is worth the effort. The course says no previous experience necessary in Python, but I recommend having at least completed a beginner's course before trying to tackle this. (Or familiarized yourself with Python if you have other programming experience - it has its quirks, like every language.)

The course will introduce you to the current state of play in machine learning and both show you what's possible and also where the limitations are. This is not a superficial course (talking points only) - you will learn enough to be dangerous. If you want to be a little safer, do the follow-on courses too. (At this time, only the 2nd course has run - regression - but it was very good).

par Daniel C

9 févr. 2016

Presenters start off kind of silly and made me wonder what I was getting into. However this class quickly evolved to be 100 times better than the course offered by U of California on Big Data. You do actual python programming through a lot of serious concepts in data analysis, visualization, and machine learning. This first course is hands on - just use the libraries. They lean heavily towards Dato which is not open source - using a 1 year trial license. However there are better instructions and support for open source in subsequent courses. Also - the second course in the series which I'm taking now is taking what we did in course 1 and diving into the math and algorithms involved - walking through actual proofs etc. It doesn't require you to know them well enough to do on your own, but they do walk you through them and explain extremely well - you actually implement the resulting algorithms. I'm fascinated by this course and can't wait to apply what I've learned.