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12,600 Bewertungen
3,017 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....


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.

16. Okt. 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

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176 - 200 von 2,936 Bewertungen für Machine Learning Foundations: A 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.

von Mitkumar P

27. Aug. 2017

This is a very well designed foundation course in the field of Machine-Learning. This course covers all the important topics of machine learning and data science from classification to deep learning and also consists of fun and interactive assignments. The instructors are very good and they have designed this course very well, I recommend people interested in machine learning field to take up this course.

von Siddharth M

18. Dez. 2015

An excellent introduction to different machine learning algorithms. As expected from an introductory course, this deals with only a top level overview of the tools, without getting bogged down with the details and mathematics of the underlying algorithms. I would recommend this course for those who want to familiarise themselves with using out of the box algorithms provided by different software packages.

von Ferenc F P

10. Jan. 2018

I was hesitating during the review between the 4 and 5 star. The only reason was that in some cases one could obtain different results with scikit learn than with Graphlab. But in the end I gave 5 stars because the course material was good and the exercises were made with real (pre-processed) data. This course is very good for both beginners and those who already have some knowledge in machine learning.

von Parag K

23. Mai 2021

Excellent structure of course - presented core concepts in very easy chunks for professionals to ingest, as well as gave real-world scenarios how knowledge can be used. The course dived deep enough to get hands dirty without delving too much into theory which might become useful as student gets overall understanding and then dive further. In all, fun approach with lot of potential for students to grow.

von Sauvage F

18. Dez. 2015

Very enjoyable course! Emily and Carlos actually succeed in giving a more than useful overview about so many kinds of tasks, algorithms and concepts of machine learning in very short time (given the material to cover!). I really loved the topics of the hands on assignments.

New to Python (I'm a R user most of the time) I also learned a lot about "the other language" of Data Science. Thanks a lot!

von Christine S

9. Nov. 2015

Course is well organized, lectures explain learning concepts very well. And using python notebook examples to show machine learning uses are very unique and quite easy to follow. The assignments may not have been as challenging as some other school's courses, but overall, this is a great course for those who would like to have a practical approach to apply machine learning to solve data problems.

von Christopher O

2. Juni 2017

The course is very organized and begins as a good introductory module. The difficulty increases throughout the course, but you are given all of the information and tools you need to respond to questions, unlike some other ML and engineering related MOOCs. You also learn some techniques that are actually useful and entertaining, like scouring wikipedia to find how similar people or articles are.


11. Apr. 2016

This is course is great and the way its been taught by professors is very cool :)

I am getting to know the use case and than how we are going to do it, rather than conventional other way round. I am so happy that we first come to know about the application and its so important for a student to know that.

Thank you so much, only problem I see is that this course should have been started earlier :)

von jackytu256

15. Dez. 2015

Its a course that provides a basic concept of what the ML is such as linear regression, classification, clustering and etc. This course not only offers the general idea for students but also implements Python-based code ( based on the all of case studies), which is an efficient approach to let me real know what the lectures talk about. Its a real nice course for the ML entry-level students.

von Konstantin G

31. Dez. 2015

Great course! Thank you guys for have been made such an easy to understand way to understand basics of ML.

The thing to improve: some assignments didn't explained in the course and I still don't know the way to discover the correct answer for the assignment for the deep learning module. The question about where the simple classification can be applicabable and there is a list of functions.

von Ellen R

3. Juni 2018

I loved this course. It did a great job of getting into really interesting applications of machine learners but staying accessible for people without a lot of previous programming experience or technological knowledge. I'd really recommend it for anyone who wants to get a well-grounded sense of some of the principal machine learning techniques that are changing the way the world works.

von Enrique C M

5. Dez. 2016

Brief but very good overview to typical Machine Learning models that are currently being used in many real applications. Nice and easy going teaching model based on case studies and lots of examples and practice during the assignments. For being only an introduction to this world, it was a quite interesting intro and now I am keen to follow up with the next parts of the specialization.

von Bhisham J M

22. Sep. 2018

I found course content and they way it is designed is perfect for anyone to easily grasp the concepts. I am from non-development background and don't have much grip on python language but it was still smooth and easy for me to progress this course by learning python basics and commands as well which is required for programming assignments. Well done coursera, keep up the good job!

von William O

27. Feb. 2017

I'm just a high school student who wants to attend University at either UC Berkeley or University of Washington as a computer science and physics double major with focus in quantum computing and A.I.. This class was a great introduction into the world of A.I. / Machine Learning. I would highly suggest taking this class to get a start with simple programming and machine learning.

von Nikhil S

20. Apr. 2020

I would give it a five star if I had not faced problem while installing turicreate but then I continue course by using graphlab but it is not free and I do not have license so I cant use it for any other purpose like to build personal things. Also I would like to build my own model rather than just implimenting a built one.

But I think it was a nice course to start in the field.

von Sherry A

13. Sep. 2017

This course is excellent. I am astounded at how well programming techniques and concepts can be taught in a MOOC. I wish these tools were available 20 years ago when I was first learning programming. The instructors were wonderful, too. I was particularly impressed with the clarity of the explanations. The assignments were challenging, too. This is not a course for slackers!

von Abdallah G

18. Jan. 2017

This course was really good I found it a really good start I also really like the way in which Emily was giving the theoretical parts then Carlos follow her with the practical part . Also Emil and Carlos have a really Excellent way in explaining the course material which make it really entertaining .

Thank you so much I really loved and Appreciated every part of the course.

von Pratyush D

6. Okt. 2015

I have been halfway through the course and it is excellent if you want to build strong foundations in Machine Learning. A basic level of experience in programming is required. Python is the medium in which programming assignments have to be done. But if you have a certain level of proficiency in any language then you won't have any problem. Anyways, great going till now.

von vinit s a

23. Nov. 2016

Amazing course. I would recommend this course to get started on machine learning and understand its applications. This course doesnt go deep into the complex mathematical theory unlike others and the instructors do a very good job in going through the material systematically. Assignments are a bit challenging but manageable since graphlab has a lot of inbuilt libraries.