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

13,083 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


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


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.

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401 - 425 sur 3,043 Avis pour Machine Learning Foundations: A Case Study Approach

par ROBIN S 1

10 déc. 2020

Thoroughly enjoyed this course! Really loved the case study approach of teaching. The instructors are excellent as well, throughout the course it felt like I was hanging out with my friends building cool stuff!

par Alessio D M

7 déc. 2015

I think the course is really COOL :) I know that it's really hard to cover so many topics, but I would have been curious about the area of reinforcement learning too. Perhaps mentioning MDPs and related models.

par Lin V

20 févr. 2016

Thank you very much for providing us this cool and exciting course. Thank you, Emily and Carlos. It opens a door for me and I've really enjoyed ML so far. Hope one day I could be part of the UW. All the best.

par Cristina E

12 févr. 2016

Very good explanations and well-thought out assignments and practical exploration. The usage of the proprietary GraphLab software was a minus, but since it was used just for exploratory purposes, no harm done.

par Hossein N S

9 févr. 2016

This course was very usefull tome as it was implemented in a way that it's easy to understand the core of the module and the subject.

I understand and it prepared me for the rest of the Machine Learning courses

par Ethan G

22 nov. 2015

This was a great intro course to the topic, and the instructors both make complicated concepts accessible. For example, the explanation of non-linear features in deep learning is extremely clear and intuitive.


23 août 2018

This will be really helpful for someone who really wants to start the ML journey and not sure where to start. The content was designed well to suit people across levels and technologies. Strongly recommended.


27 juil. 2018

To define how machines can learn, we need to define what we mean by “learning.” In everyday parlance, when we say learning, we mean something like “gaining knowledge by studying, experience, or being taught.”

par Carlos A M

18 janv. 2021

Pretty Cool as Foundations in ML!!! If you already have expertize on Pandas and python you would find this course as a good entry point for Machine Learning as the point of view is 50/50 theorical/practice.

par Adrian L

10 juil. 2020

Friendly introduction to basic concepts and how to put them in practice to start diving into the exciting ML world that is all around us nowadays, specifically during current uncertain and challenging times.

par Lokesh K

26 janv. 2019

I appreciate the effort you kept for this online course.Actually I enjoyed learning here.But you can be little bit more detailed in the ipython notebook code explanation. Otherwise ,this is the best course .

par Ramesh K

8 févr. 2016

Course is really taking a practical approach towards machine learning, with theory and practical classes side by side. Thanks to Course era and University of Washington for providing a wonderful opportunity.


21 juin 2020

This course will provide a deep and elaborated knowledge about basics of machine learning and deep learning. Both the instructor Emily And Carlos are very good they cover each and every point of discussion.


8 juin 2020

Such an amazing course.

It opens all the uncovered secrets behind Machine Learning .

With best mentors and enough practice i had gain thorough knowledge and interest toward Machine Learning.

Above Expectation.

par Muhammad H T

4 mai 2020

Amazing course by Carlos Guestrin and Emily Fox both have the deep knowledge of their domain and more over they also have the skills of how to teach. Love you both Carlos Guestrin (Sir) and Emily Fox (Mam)

par Rania B

6 janv. 2019

I had to use TuriCreate instead of GraphLab, so other than the changes in the libraries that had me guessing which function to use, everything in this course is well structured and concrete. Thank you all!

par 黄怡

29 mai 2018

Actually, this course is the best introduction for machine learning for me .

it gives me a outline of machine learning structure . thankful , and i will continue learn other courses in this whole course .

par Olga V

7 juil. 2017

Great course giving an overview helping get a sense how machine learning is applied. Material is delivered well and concisely. Like the data sets used for examples, because they are interesting to explore.

par Eik U H

27 juin 2017

A real breathtaking great course about the basics of machine learning with very concise materials. Unfortunately died after four parts. I'am hoping for resurrection with a part 5 and 6.

Thank you very much.

par Lucas d L O

9 août 2016

Great course for understanding introductory principles of the different areas of Machine Learning. The classes are very well taught and the exercises are very interesting. Highly recommended for beginners!

par Guillermo R

13 mai 2018

I really enjoyed the foundations course. It did exactly as I expected - it gave a great overview of machine learning concepts to prepare for the upcoming in-depth modules. Emily and Carlos were fantastic!

par Kan B

24 oct. 2016

Very good approach. Let students hands on and play with ML model first, before jumping into details. In real life, understanding use cases is really important before investigating more time into theories.

par jeevanjot s

29 oct. 2018

Very good foundations course for beginners.....might be a little too basic for people who have experience in ML, but nonetheless good for refreshing your knowledge. Absolutely love the sue case approach.

par Pranav V V

15 oct. 2017

The course provides a good overview of different ML approaches - Regression, Classification, Clustering, Neural Networks. The approach of using exercises to answer quiz helps in practicing the concepts.

par Eftychios V

25 juin 2016

I really liked the case study approach. It starts from real life examples and shows you how simple it is to make your own models and predictions which really lures you into the machine learning concept.