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12,076 Bewertungen
2,892 Bewertungen

Über den Kurs

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....


19. Dez. 2016

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

18. Aug. 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

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151 - 175 von 2,804 Bewertungen für Machine Learning Foundations: A Case Study Approach

von Целых Л А

8. Mai 2020

It was a really rewarding course! Although I participate with my colleagues and partners in the development of decision-making methods based on a completely different approach, this course was useful for me to reflect on my own scientific position and to increase my competence in achieving my scientific goals. Although, apparently, the main goals for all of us are the same. The teachers were very charismatic! Course - available for understanding and implementation. Thank you so much!

von Jun Q

13. Jan. 2016

I am Jun Qi, a Ph.D. student in the department of Electrical Engineering at University of Washington. I ever took Carlo's excellent machine learning courses at UW and was really feeling pretty good. Although I may become an intermidiate machine learning researcher, I am of great interest in taking his new on-line courses because his new courses are more pracitcal and focus on large-scale data processing. So I highly recommend Prof. Carlos's machine learning courses on the Coursera.

von 朱顺

19. Dez. 2015

Two things make me follow this specialization.

Firstly, the Final Capstone mentioned in this course excites me for it is more likely to build a product rather than just to understand some concepts from doing a little programming assignments

Secondly, these techniques are very useful and cool.

But I think this specialization lasts too long and two weeks' material could be done within one week. It is more helpful if other courses in this series would be opened as soon as possible.

von Iñaki D R

21. Juni 2020

Uno de los mejores cursos para poder entender y practicar en un nivel principiante las principales técnicas de machine learning. Los profesores son excelentes en sus explicaciones y mantienen un gran interés en la clase y sus actividades. Las evaluaciones son objetivas y siempre te brindan el material de apoyo necesario para poder realizarlas. Estoy muy animado de haber cursado este curso y lo considero con la preparación suficiente para tomar los cursos que le siguen.

von Muhammad U I

24. Okt. 2016

For me, this course excelled at brushing up ML concepts I had studied years ago and clarifying the appropriateness of different techniques for different problem settings. However, the best part about this course, and the reason I took it in the first place, was that it introduces participants to a new tool that is scalable for use in larger / production systems.

I am much obliged to the instructors and am sure to continue on to the next course in this specialization.

von Bayardo E Q T

30. Juli 2020

First of all, sorry for my English. The course is a very interesting first experience with the machine learning and give a base by

all the panorama, the class are funny and easy to understand, and the professors are excellent with the explanation. I really recommend this course for all the people that be start with this new world that is the ML.

Pd. by the way, for the people that no speak a very well English, don't worry, is easily to understand both professors.

von Leonardo M d O

25. Aug. 2018

Amazing course. I had already done other ML Courses at coursera, but the competitive differential is the friendly approach took by the professors. Carlos and the other girl are very nice, they the training gets less formal, they look like a friend telling stories in a bar. Another main point is really the uses cases. They swap between the big forest map and the detailed view of the leaf in a succinct way. Easy to understand both views. Congratulations.

von Himadri M

17. Dez. 2015

The Course literally boosts off one's confidence in ML, and gives one the confidence to proceed to higher levels in ML.

Great Course, Superb Instructors and Excellent Course material ! The complete ingredient for a perfect starter course !

I would like to mention that i came to know how to write a review using the words: Great , Good , Superb and Excellent , so that my review is rated above others by the algorithm !

So thanks Carlos sir for this superb course ! :D

von Gurmeet S G

10. Juli 2020

A good start to ML. Light on the programming / algorithms but that helps focus on the concepts which is appropriate for a complete beginner to ML.

It took a while to get set up because of the Python and turicreate versions for Mac OSX so I almost dropped out of this course. Glad I kept to it though. Maybe more value in using the standard packages to make skills comparable to others, but I appreciate that turicreate has been optimised for large datasets.

von Abhijit D

25. Nov. 2017

Excellent course presentation by Emily and Carlos - If courses are presented in this interactive manner learning will always be fun and interesting.

Always advisable to have some basics on python , data frame , machine learning(if possible) and you will go really smooth with this intermediate level course.

Course material really good for machine learning with real case studies and capstone project on deep learning was indeed the crown of the course.

von Louis U

15. Nov. 2017

Absolutely awesome! I am really appreciative of the time and efforts on the part of the instructors and the University of Washington to make Machine Learning very accessible. The concepts were very easy to grasp and I endorse the case study approach as a effective introduction to complex topics. Obviously, it will get more detailed and complex in upcoming courses in the specializations but I feel very prepared and excited to learn. Thank you.

von Syed M Z H K

5. Aug. 2017

Thanks alot for this awesome course. As because of it, I was able to learn python (otherwise I used to hate it, when I started learning it with OpenCV) and ipyhon (which is an awesome tool). Furthermore, thanks alot course era for providing me with this amazing fee waiver (since I can't afford this course) , as because of this I am hoping to excel in this field after completing this specialization, in order to later land good job. Thank you!

von Prachur B

27. Dez. 2016

A very practical approach for learning and get excited about Machine Learning. The python notebook exercises really help if you do them diligently (though sometimes it was too easy because of hints, may be hide them and who when someone asks for it). The mention of so many concepts and algorithms can be overwhelming, so a clear guideline on how to leverage the material specifically in this foundation course in the closing remarks would help.

von AMAN M

18. Dez. 2018

I was totally new to the machine learning, but this course helped me to understand what is it? What is the importance of it ? where it can be used and what will be the future of it ? There was also enough exercise work to check our understanding to the topic learnt. I think it will be more interesting if they provide a console for code snippet for the assignment... It was very nice experience with Carlos Guestrin Sir and Emily Fox Ma'am

von Tobi L

6. Dez. 2015

I appreciate that the first course focused on applications, I've got plenty of math and programming experience, but I took this specialization to really grok machine learning and its applications. By using graphlab as a black box and focusing on specific applications, I really understood why these techniques are useful. Once I've got the why, I feel much more motivated to dig deeper into the how, which I feel confident enough that I can do.

von Aleksander S

1. Feb. 2019

This is a great course. The content is delivered at a very good pace even for people with little prior knowledge of statistics or computer science — not too fast (would be too difficult) and not too slow (could become boring). Additionally, the assignment model is perfect — it requires completing hands-on exercises, but then the solution is assessed using simple quizzes. Thanks to that the answers and the grades are immediately available.

von George C

26. Dez. 2015

The case study approach and the reliance on GraphLab library makes it easier to get your head around the concepts before going into the detail later in the specialization. I learn better when I have a working understanding of the high-level concepts and the use for a new area of study. This course provides that high-level understanding and the later specializations provides the deep dive. Also, the course seemed well paced and structured.

von Chengcheng L

27. Dez. 2015

This is a wonderful course to get you into the door of machine learning. It covers several key concepts in ML. The videos are easy to follow. The assignments are not difficult to complete if you do the "follow along" exercises. You won't be able to understand the theoretical background of the algorithm very well after taking this course, but you can apply Grahphlab functions to whatever data you have and generate quick and dirty results.

von Evan S

11. März 2019

This course was a great balance between lecture (and lecture quiz) & iPython lecture (and iPython lecture quiz). I like that the answers are multiple choice as opposed to copying and pasting code. That way, any coding errors can be played around with in the notebook first without using up any submission attempts. Emily and Carlos did a great job of keeping the course fun while sticking to the easy-to-understand case-study approach.

von Divyansh S

25. Dez. 2018

I found this course advantageous for me. I found the case study approach of teaching the various concepts of Machine Learning quite helpful. Case Study approach gives us the idea of practical implementaton of these concepts in real life. The quality of the teaching content was very good. Moreover the assignments helped a lot in understanding some of the key concepts. Ideal course for newbies to start learning Machine Learning.

von Matthew S

7. Jan. 2020

A well rounded and not intimidating approach to machine learning. The concepts are introduced clearly and succinctly. The exercises are relevant and digestible. I feel like I have a much better understanding of the concepts to build upon. The only thing I would have liked to see is more outside reading on things that were introduced, but that's also in the next courses of the specialization or just a google away.

von Dhananjay M

7. Feb. 2016

It is an amazing course being taught by professor Emily and professor Carlos. What sets this course apart from any other MOOCs or classes is the case study approach to explain the algorithms. Learning is most productive when a person can visualize what he is taught. This is exactly what this course does by helping students see what they can do with the algorithms they learning with this case-study based approach.

von Allen C L

17. Juni 2016

A very nice introductory course that uses real-world use case examples to illustrate foundational concepts in machine learning. If, like me, you have only an inkling about what is machine learning, this is a good course to give you a broad overview. Along the way, you'll pick up some very useful Python skills for use in data analysis. You'll also learn to use the nice Python tool, the iPython (Jupyter) Notebook.

von Christopher M

6. Dez. 2018

This was a great course. The instructors were fun and knowledgeable and the assignments were well-written. I loved the flexibility of being allowed to use whatever software I wanted to solve the ML assignments since the quizzes were based on the results of the modeling rather than submitted code. For some assignments I used sklearn and for others I used the software recommended by the instructors (graphlab).

von Joseph C

5. Dez. 2015

Excellent overview course, introducing the ideas of regression, classification, clustering, recommender systems, and a sort of 'short cut' of using the early layers pretrained deep neural network for image recognition as feature inputs into a classifier. Don't expect to get into the 'details' of implementation in this overview course; I believe that level of detail will be covered in the subsequent courses.